face recognition system using LBP

Feb 9, 20119 likes11,472 views

This document outlines the implementation plan and milestones for a final year project on face recognition using local binary pattern (LBP) features. The project involves enrolling images by extracting LBP histograms and storing them in a matrix. It then tests the matching algorithm on low quality images and evaluates for false and positive matches. The conclusion reflects on lessons learned about LBP algorithms and face recognition challenges with image quality.

Final Year Project Face Recognition Using Local Features Marwan Noman 1051108336

Implementation Plan Milestones of Phase II:

Solution - Enrollment Process: 1. The images were enrolled by applying LBP function. 2. The histograms of these images will be stored as vectors in a matrix. 3. The distance function will compare two matrixes from the same dimensionality to get matching image.

Solution (3) Feature Extraction Algorithm:

Implementation Process Extracted Histogram:

Implementation Process (2) The LBP is used to extract the local important features of the images. Hence, it will convert the result to histogram values. These values will be stored in a matrix as values.

Implementation Process (3) - Enrollment and Recognition processes : - There are around 96 images of different subjects. - There are 12 different subjects. Each subject has 8 different images. - An image will be used of each subject for recognition purposes.

Implementation Process (4) - Basic Design:

Implementation Process (5) - Finalized Design:

Implementation Process (6) - Loading the image :

Implementation Process (7) - Extracting the image:

Implementation Process (8) - Scanning the image:

Conclusion - Great experience of Face Recognition systems . - Achievement of a good level of understanding LBP algorithm, and it was a good opportunity for me to distinguish between local features and global features of images . - One of the problems was faced during the testing, is image quality.

Conclusion (2) - Euclidean Distance was the solution by finding the distance between histograms of the original image and the enrolled images . - By the minimum distance between the histograms, it will find the most matching image of the original one. - It is compulsory to crop bad quality images .

Object detection is a computer vision technique that identifies objects in images and videos. It can detect things like faces, humans, buildings, and cars. Object detection has applications in areas like image retrieval, video surveillance, and face detection. Image processing techniques are used to both improve images for human interpretation and to make images more suitable for machine perception. These techniques include enhancing edges, converting images to binary, greyscale, or true color formats. Face detection is a common application that finds faces in images and ignores other objects. It is often used as the first step in face recognition systems.

Detection and recognition of face using neural networkSmriti Tikoo

This document describes research on face detection and recognition using neural networks. It discusses using the Viola-Jones algorithm for face detection and a backpropagation neural network for face recognition. The Viola-Jones algorithm uses haar features, integral images, AdaBoost training, and cascading classifiers for real-time face detection. A backpropagation network with sigmoid activation functions is trained on facial images for recognition. Results show the network can accurately recognize faces after training. The document concludes the approach allows face recognition from an input image and discusses limitations and potential improvements.

Image processing fundamentalsDr. A. B. Shinde

This document provides an overview of digital image processing. It defines what an image is, noting that an image is a spatial representation of a scene represented as an array of pixels. Digital image processing refers to processing digital images on a computer. The key steps in digital image processing are image acquisition, enhancement, restoration, compression, morphological processing, segmentation, representation, and recognition. Digital image processing has many applications including medical imaging, traffic monitoring, biometrics, and computer vision.

Elements of visual perceptionDr INBAMALAR T M

The document discusses elements of visual perception including the structure and function of the human eye and visual system. It describes how (1) light is focused through the cornea and lens onto the retina, where rods and cones detect the image and transmit signals to the brain, (2) the fovea provides sharp central vision while peripheral vision is supported by rods, and (3) brightness adaptation allows the eye to perceive a wide range of intensities through changes in sensitivity. Phenomena like Mach bands and simultaneous contrast demonstrate that perceived brightness depends on context rather than absolute intensity.

Image segmentation Tubur Borgoary

This document summarizes techniques for image segmentation based on global thresholding and gradient-based edge detection. It discusses image segmentation, approaches like thresholding and edge detection in MATLAB. Thresholding is demonstrated on sample images to extract objects at different threshold values. Edge detection is also shown using Sobel filters. Issues like segmenting similar objects and boundary detection in the presence of noise are mentioned.

Automated Face Detection SystemAbhiroop Ghatak

The document summarizes an OpenCV based image processing attendance system. It discusses using OpenCV to detect faces in images and recognize faces by comparing features to a database. The key steps are face detection using Viola-Jones detection, face recognition using eigenfaces generated by principal component analysis to project faces into "face space", and measuring similarity by distance between projections.

Image segmentation pptGichelle Amon

This document provides an introduction to image segmentation. It discusses how image segmentation partitions an image into meaningful regions based on measurements like greyscale, color, texture, depth, or motion. Segmentation is often an initial step in image understanding and has applications in identifying objects, guiding robots, and video compression. The document describes thresholding and clustering as two common segmentation techniques and provides examples of segmentation based on greyscale, texture, motion, depth, and optical flow. It also discusses region-growing, edge-based, and active contour model approaches to segmentation.

Edge linking in image processingVARUN KUMAR

This document discusses image segmentation techniques, specifically linking edge points through local and global processing. Local processing involves linking edge-detected pixels that are similar in gradient strength and direction within a neighborhood. Global processing uses the Hough transform to link edge points into lines by mapping points in the image space to the parameter space of slope-intercept or polar coordinates. Thresholding in parameter space identifies coherent lines composed of edge points. The Hough transform allows finding lines even if there are gaps or other defects in detected edge points.

Image segmentation based on coloreSAT Journals

Abstract Image Segmentation plays a vital role in image processing. The research in this area is still relevant due to its wide applications. Image segmentation is a process of assigning a label to every pixel in an image such that pixels with same label share certain visual characteristics. Sometimes it becomes necessary to calculate the total number of colors from the given RGB image to quantize the image, to detect cancer and brain tumour. The goal of this paper is to provide the best algorithm for image segmentation. Keywords: Image segmentation, RGB

Texture based feature extraction and object trackingPriyanka Goswami

This document provides a project report on texture-based feature extraction and object tracking. It discusses using various texture analysis techniques like Local Binary Pattern (LBP), Local Derivative Pattern (LDP), and Local Ternary Pattern (LTP) to extract features from images for tasks like cloud tracking. It implements these techniques in MATLAB and evaluates them on standard datasets to extract features and represent images with histograms for tasks like image recognition and analysis while reducing computational requirements compared to using raw images. The techniques are then applied to track cloud motion in weather satellite images by analyzing differences in texture histograms over time.

Face recognisation systemSaumya Ranjan Behura

This document discusses face recognition systems and the use of artificial neural networks for face recognition. It describes the basic steps in a face recognition system as face detection, alignment, feature extraction, and matching. Two types of neural networks that can be used for recognition are described - Radial Basis Function Networks and Back Propagation Networks. RBF Networks have an input, hidden, and output layer while BPN uses backpropagation of errors to adjust weights. The document also outlines some applications of face recognition systems such as ID verification and criminal investigations.

Introduction to image contrast and enhancement methodAbhishekvb

This document discusses various methods for contrast enhancement of digital images. It defines contrast and describes how contrast is important for distinguishing objects from backgrounds. Two main categories of contrast enhancement methods are described: spatial domain methods, which operate directly on pixel values; and frequency domain methods, which modify the Fourier transform of an image. Specific spatial techniques covered include logarithmic transformation, power law transformation, gamma correction, and histogram equalization. Contrast stretching is also discussed. Examples of code implementations and results are provided. Contrast enhancement has applications in medical imaging, surveillance, and other fields where improving image interpretability is important.

HogAnirudh Kanneganti

The document describes the Histogram of Oriented Gradients (HOG) feature descriptor technique. HOG counts occurrences of gradient orientation in localized portions of an image to represent a distribution of intensity fluctuations along different orientations. It works by first calculating gradient images, then calculating histograms of gradients in 8x8 cells, followed by block normalization to account for lighting variations before forming the final HOG feature vector.

Lect 02 first portionMoe Moe Myint

This document provides an overview of a digital image processing lecture given by Dr. Moe Moe Myint at Technological University in Kyaukse, Myanmar. It includes information about the instructor's contact information and office hours. The document then summarizes the contents of Chapter 2, which covers topics like visual perception, light and the electromagnetic spectrum, image sensing and acquisition, and basic relationships between pixels. Examples and diagrams are provided to illustrate concepts like the structure of the human eye, image formation, brightness adaptation, and the electromagnetic spectrum. Optical illusions are also discussed as examples of how visual perception does not always match physical stimuli.

Lect 02 first portionMoe Moe Myint

AnimationVasu Mathi

Computer animation involves creating animation sequences through object definition, path specification, key frames, and in-betweening. There are two main methods for displaying animation sequences: raster animation and color-table animation. Raster animation involves copying frames from memory to the display very quickly, while color-table animation uses a color lookup table to convert logical color numbers in each pixel to physical colors. The document discusses techniques for designing animation sequences like storyboarding, defining objects and paths, specifying key frames, and generating in-between frames. It also covers topics like motion specification using direct motion, goal-directed systems, kinematics, dynamics, and inverse kinematics. Morphing and tweening are introduced as techniques for warping one image into

Intensity Transformation and Spatial filteringShajun Nisha

Dr. S. Shajun Nisha discusses intensity transformation and spatial filtering techniques in image processing. Intensity transformation functions modify pixel intensities based on a transformation function. Spatial filtering involves applying an operator over a neighborhood of pixels. Common intensity transformations include contrast stretching and logarithmic transforms. Histogram equalization is also described to improve contrast. Spatial filters include linear filters implemented using imfilter and non-linear filters like median filtering with ordfilt2 and medfilt2. Examples demonstrate applying these techniques to enhance images.

Spatial Filters (Digital Image Processing)Kalyan Acharjya

ImageProcessing10-Segmentation(Thresholding) (1).pptVikramBarapatre2

This document discusses image thresholding techniques for image segmentation. It describes thresholding as the basic first step for segmentation that partitions an image into foreground and background pixels based on intensity value. Simple thresholding uses a single cutoff value but can fail for complex histograms. Adaptive thresholding divides an image into sub-images and thresholds each individually to handle varying intensities better than simple thresholding. The document provides examples and algorithms to illustrate thresholding and its limitations and adaptations.

Image RestorationPoonam Seth

The document discusses image restoration techniques. It describes how images can become degraded through phenomena like motion, improper camera focusing, and noise. The goal of image restoration is to recover the original high quality image from its degraded version using knowledge about the degradation process and types of noise. Common noise models include Gaussian, Rayleigh, Erlang, exponential, and impulse noise. Filtering techniques like mean, order statistics, and adaptive filters can be used for restoration by smoothing the image while preserving edges. The adaptive filters change based on local image statistics to better reduce noise with less blurring than regular filters.

Fundamentals steps in Digital Image processingKarthicaMarasamy

This document provides an overview of key concepts in digital image processing, including: 1. It discusses fundamental steps like image acquisition, enhancement, color image processing, and wavelets and multiresolution processing. 2. Image enhancement techniques process images to make them more suitable for specific applications. 3. Color image processing has increased in importance due to more digital images on the internet. Wavelets allow images to be represented at various resolution levels.

IMAGE SEGMENTATION.Tawose Olamide Timothy

Image filtering in Digital image processingAbinaya B

This document discusses various image filtering techniques used for modifying or enhancing digital images. It describes spatial domain filters such as smoothing filters including averaging and weighted averaging filters, as well as order statistics filters like median filters. It also covers frequency domain filters including ideal low pass, Butterworth low pass, and Gaussian low pass filters for smoothing, as well as their corresponding high pass filters for sharpening. Examples of applying different filters at different cutoff frequencies are provided to illustrate their effects.

Image compression .Payal Vishwakarma

This document provides an overview of digital image processing and image compression techniques. It defines what a digital image is, discusses the advantages and disadvantages of digital images over analog images. It describes the fundamental steps in digital image processing as well as types of data redundancy that can be exploited for image compression, including coding, interpixel, and psychovisual redundancy. Common image compression models and lossless compression techniques like Lempel-Ziv-Welch coding are also summarized.

Image feature extractionRishabh shah

Image Filtering in the Frequency DomainAmnaakhaan

Hough Transform By Md.Nazmul IslamNazmul Islam

The Hough transform is a feature extraction technique used in image analysis and computer vision to detect shapes within images. It works by detecting imperfect instances of objects of a certain class of shapes via a voting procedure. Specifically, the Hough transform can be used to detect lines, circles, and other shapes in an image if their parametric equations are known, and it provides robust detection even under noise and partial occlusion. It works by quantizing the parameter space that describes the shape and counting the number of votes each parametric description receives from edge points in the image.

Local binary patternInternational Islamic University

Divide the examined window into cells (e.g. 16x16 pixels for each cell). 2- For each pixel in a cell, compare the pixel to each of its 8 neighbors (on its left-top, leftmiddle, left-bottom, right-top, etc.). Follow the pixels along a circle, i.e. clockwise or counterclockwise. 3- Where the center pixel's value is greater than the neighbor's value, write "1". Otherwise, write "0". This gives an 8-digit binary number (which is usually converted to decimal for convenience). 4- Compute the histogram, over the cell, of the frequency of each "number" occurring (i.e., each combination of which pixels are smaller and which are greater than the center).

Face recognition technology - BEST PPTSiddharth Modi

Face recognition technology may help solve problems with identity verification by analyzing facial features instead of passwords or pins. The document outlines the key stages of face recognition systems including data acquisition, input processing, and image classification. It also discusses advantages like convenience and ease of use, as well as limitations such as an inability to distinguish identical twins. Potential applications are identified in government, security, and commercial sectors.

More Related Content

What's hot (20)

Edge linking in image processingVARUN KUMAR

Image segmentation based on coloreSAT Journals

Texture based feature extraction and object trackingPriyanka Goswami

Face recognisation systemSaumya Ranjan Behura

Introduction to image contrast and enhancement methodAbhishekvb

HogAnirudh Kanneganti

Lect 02 first portionMoe Moe Myint

AnimationVasu Mathi

Intensity Transformation and Spatial filteringShajun Nisha

Spatial Filters (Digital Image Processing)Kalyan Acharjya

ImageProcessing10-Segmentation(Thresholding) (1).pptVikramBarapatre2

Image RestorationPoonam Seth

Fundamentals steps in Digital Image processingKarthicaMarasamy

IMAGE SEGMENTATION.Tawose Olamide Timothy

Image filtering in Digital image processingAbinaya B

Image compression .Payal Vishwakarma

Image feature extractionRishabh shah

Image Filtering in the Frequency DomainAmnaakhaan

Hough Transform By Md.Nazmul IslamNazmul Islam

Edge linking in image processingVARUN KUMAR

Image segmentation based on coloreSAT Journals

Texture based feature extraction and object trackingPriyanka Goswami

Face recognisation systemSaumya Ranjan Behura

Introduction to image contrast and enhancement methodAbhishekvb

HogAnirudh Kanneganti

Lect 02 first portionMoe Moe Myint

AnimationVasu Mathi

Intensity Transformation and Spatial filteringShajun Nisha

Spatial Filters (Digital Image Processing)Kalyan Acharjya

ImageProcessing10-Segmentation(Thresholding) (1).pptVikramBarapatre2

Image RestorationPoonam Seth

Fundamentals steps in Digital Image processingKarthicaMarasamy

IMAGE SEGMENTATION.Tawose Olamide Timothy

Image filtering in Digital image processingAbinaya B

Image compression .Payal Vishwakarma

Image feature extractionRishabh shah

Image Filtering in the Frequency DomainAmnaakhaan

Hough Transform By Md.Nazmul IslamNazmul Islam

Viewers also liked (20)

Local binary patternInternational Islamic University

Face recognition technology - BEST PPTSiddharth Modi

Face recognition pptSantosh Kumar

This document provides an overview of facial recognition technology. It discusses the history of facial recognition, how the technology works by detecting nodal points on faces and creating faceprints for identification. It also covers implementations, comparing images to templates to verify or identify individuals, and applications in security and surveillance. Strengths are its non-invasive nature, but it can be impacted by changes in appearance.

Facial expression recognition based on local binary patterns finalahmad abdelhafeez

This document summarizes research on facial expression recognition using Local Binary Patterns (LBP) features. The key points discussed are: 1) LBP features are effective and efficient for facial expression recognition compared to other methods like Gabor wavelets. 2) LBP features perform robustly even at low image resolutions, important for real-world applications. 3) Boosting LBP features improves recognition performance over using LBP alone. However, boosted features may not generalize well across datasets. The paper presents a comprehensive study of LBP features for facial expression recognition and addresses challenges like low-resolution images.

Lbp based edge-texture features for object recoginitionIGEEKS TECHNOLOGIES

Facial expression recognition based on image featureTasnim Tara

This document presents a method for facial expression recognition based on image features. It discusses existing works that use techniques like PCA and Gabor wavelets for feature extraction and Euclidean distance for classification. The proposed method uses Gaussian filtering, radial symmetry transform, and edge projection for feature extraction, and calculates a feature vector based on geometric facial parameters to classify expressions using Euclidean distance. It aims to recognize six basic expressions accurately from the JAFFE database and could be developed for real-time video recognition in the future.

Face recognitionSatyendra Rajput

This document summarizes a student project on face recognition. It begins with an introduction to face recognition, its applications, and common challenges. It then reviews literature on existing face recognition methods and identifies problems related to tilted poses and variations in illumination and expression. The proposed method will work to improve recognition rates under these conditions in two phases - training and testing. The method aims to enhance the preprocessing and feature extraction steps to make the system more robust. A basic flowchart of the proposed approach is provided, and the document concludes with references.

Face recognition tech1Ankit Gupta

Image processing research proposalIftikhar Ahmad

This document outlines the requirements specification for a research project on video watermarking. The main aims of the project are to address copyright protection of digital video and develop a video watermarking scheme based on the Discrete Wavelet Transform using Matlab Simulink. The goals are to embed a watermark imperceptibly into video while making it robust against various attacks. The justification is that video faces increased attacks compared to other media. The work schedule outlines tasks from February to June 2015 including research, planning, analysis, design, coding and testing.

Face Recognition Proposal PresentationMd. Atiqur Rahman

This document outlines a project on face recognition. It discusses biometrics and why face recognition was chosen over other methods. Applications of face recognition are presented for law enforcement, security, immigration, residential security, banking, and mobile unlocking. The working process of face recognition involves inputting an image, detecting the face, and recognizing the face by matching it to a database. The current progress includes detecting faces and face parts using a modified Viola-Jones algorithm. Future work will involve matching detected faces to images in a created database for recognition. Advantages of face recognition include convenience and limitations include inability to distinguish identical twins.

Eigenface For Face RecognitionMinh Tran

The document discusses the eigenface approach for face recognition. It provides an overview of eigenfaces, how they are calculated from a training set of faces, and how they can be used to identify faces by projecting faces onto the eigenface space. Major steps include calculating the eigenfaces from a training set, projecting new images into eigenface space to get weight coefficients, and comparing the weights to known individuals' weights or thresholds to classify faces as known or unknown. Advantages are ease of implementation and little preprocessing required, while limitations include sensitivity to head scale and only applicable to frontal views under controlled conditions.

Face Recognition using PCA-Principal Component Analysis using MATLABSindhi Madhuri

PCA is used for face recognition. It involves calculating eigenvectors from a training set of face images to define a feature space called "eigenfaces". A new face is recognized by projecting it onto this space and comparing to existing faces. PCA works by identifying directions of maximum variance in the training data, capturing the most important information about faces with fewer vectors. Potential applications include identification, security, and human-computer interaction. However, it is sensitive to changes in lighting and expression.

Face recognition using neural networkIndira Nayak

This document summarizes a seminar presentation on face recognition using neural networks. It discusses face recognition, neural networks, the steps involved which include pre-processing, principle component analysis, and back propagation neural networks. Advantages of neural networks for face recognition are robustness to variations in faces and ability to learn from data. Face recognition has applications in security and identification.

face recognitionvipin varghese

Face recognition vaishaliVaishali Bansal Bhati

This document discusses face recognition using principal component analysis (PCA). It begins by defining face recognition and distinguishing it from face detection. It then outlines the steps of the PCA algorithm for face recognition, including representing faces as vectors, calculating the average face vector, normalizing faces, calculating the covariance matrix, selecting eigenvectors to reduce dimensionality, projecting faces into the reduced eigenface space, and representing faces as a linear combination of eigenfaces. The document focuses on explaining the PCA algorithm and its steps for performing eigenface-based face recognition.

Face recognition and retrieval using cross age reference coding with cross-ag...I3E Technologies

Iaetsd efficient retrieval of face image fromIaetsd Iaetsd

This document proposes two methods for efficient retrieval of face images from large scale databases: attribute-enhanced sparse coding (ASC) and attribute-embedded inverted indexing (AEI). ASC uses automatically detected human attributes to generate codewords that provide semantic descriptions of faces during offline processing. AEI constructs an inverted index for efficient online retrieval using the codewords. Reranking is also used to discard forged images and improve retrieval accuracy. Experimental results demonstrate the proposed system can efficiently retrieve relevant face images in milliseconds while eliminating forged images through reranking.

Single Sample Soft Shadows Using Depth Mapsstefan_b

This paper presents a method for rendering single sample soft shadows using depth maps. The method adapts Parker's idea of using an outer hull for objects to hardware rendering. It uses sampled depth maps from the light's perspective to compute hard shadows and then adds outer and inner penumbra regions. To add the penumbra regions, it searches the depth map neighborhood around shadowed pixels to attenuate lighting based on distance to hard shadows or occluders. This approach generates soft shadows at interactive frame rates using only a single sample per light source.

Expression invariant face recognitionSumit Agarwal

This document discusses using Local Binary Patterns (LBP) for facial expression recognition from images. It summarizes that LBP is used to extract features from facial regions that are divided into cells. Histograms of these LBP features are concatenated into a feature vector for each image. Support Vector Machines (SVM) are used to classify the images into six basic expressions based on these vectors, achieving better results than template matching or Linear Discriminant Analysis. The document also proposes a method for expression-invariant face recognition that classifies an input image, neutralizes it, then searches a neutral image dataset to find potential matches.

PreprocessingTejas Bubane

The document outlines the key steps in preprocessing text data: 1. Tokenization breaks text into individual words by removing punctuation and numbers and splitting on spaces. 2. Stop words, like "the" and "an", are removed to focus on meaningful words. 3. Stemming reduces words to their root form using an algorithm like Porter stemming. This groups related words together but does not always find the true root. 4. A vocabulary is created by taking the union of all words from all documents after stemming to prepare for further analysis.

Local binary patternInternational Islamic University

Face recognition technology - BEST PPTSiddharth Modi

Face recognition pptSantosh Kumar

Facial expression recognition based on local binary patterns finalahmad abdelhafeez

Lbp based edge-texture features for object recoginitionIGEEKS TECHNOLOGIES

Facial expression recognition based on image featureTasnim Tara

Face recognitionSatyendra Rajput

Face recognition tech1Ankit Gupta

Image processing research proposalIftikhar Ahmad

Face Recognition Proposal PresentationMd. Atiqur Rahman

Eigenface For Face RecognitionMinh Tran

Face Recognition using PCA-Principal Component Analysis using MATLABSindhi Madhuri

Face recognition using neural networkIndira Nayak

face recognitionvipin varghese

Face recognition vaishaliVaishali Bansal Bhati

Face recognition and retrieval using cross age reference coding with cross-ag...I3E Technologies

Iaetsd efficient retrieval of face image fromIaetsd Iaetsd

Single Sample Soft Shadows Using Depth Mapsstefan_b

Expression invariant face recognitionSumit Agarwal

PreprocessingTejas Bubane

Similar to face recognition system using LBP (20)

EE-2018-1303261-1.pdfUmarDrazKhan2

Here are the key problems with traditional student attendance marking techniques: - Calling out student names disrupts the teaching process and causes distraction for students. This is especially problematic during exam sessions. - Passing around an attendance sheet during lectures, especially in large classes, can be difficult to manage efficiently. - Relying on manual methods increases the chance of human errors in marking attendance. Names may be misspelled or students accidentally missed. - There is no automated recording of attendance. The data needs to be manually compiled and recorded afterwards. To address these issues, an automated face recognition based student attendance system is proposed. It aims to simplify the attendance marking process, reduce human errors, and automatically record attendance

IRJET- DigiyathraIRJET Journal

This document describes a facial recognition and biometric security system called Digiyathra that is intended to streamline airport security checks. It would allow passengers to complete check-in, bag drop, and boarding using only their face as identification. During online ticket booking, passengers would submit a passport photo that would be added to a database and used for verification at various points throughout their journey. This system aims to accelerate passenger throughput while reducing costs by minimizing the need for paper-based ID checks. It provides details on how facial recognition works, describing the five main steps of detection, analysis, template generation, matching, and result determination. Local Binary Patterns Histograms are discussed as the specific method used to recognize and identify faces within this

Face Identification for Humanoid Robotthomaswangxin

Face Recognition System Using Local Ternary Pattern and Signed Number Multipl...inventionjournals

This paper presents a novel approach to face recognition. The task of face recognition is to verify a claimed identity by comparing a claimed image of the individual with other images belonging to the same individual/other individual in a database. The proposed method utilizes Local Ternary Pattern and signed bit multiplication to extract local features of a face. The image is divided into small non-overlapping windows. Processing is carried out on these windows to extract features. Test image’s features are compared with all the training images using Euclidean's distance. The image with lowest Euclidean distance is recognized as the true face image. If the distance between test and all training images is more than threshold then test image is considered as unrecognised image or match not found .The face recognition rate of proposed system is calculated by varying the number of images per person in training database

Project presentation by Debendra AdhikariDEBENDRA ADHIKARI

This document outlines a research project proposal for implementing real-time face recognition on an attendance system. The project aims to use machine learning and computer vision techniques to detect student faces and recognize their names for attendance tracking. The proposal discusses conducting an initial prototype using Python, OpenCV, NumPy and local binary pattern (LBP) classification. It describes collecting a database of facial images, developing the system design using use case, activity and sequence diagrams. The work plan outlines developing the prototype over several months. The goal is to gain experience with computer vision tools and apply face recognition to applications like security, banking and more.

SYNOPSIS on face recognition attendance system softwarezalam5577

A face recognition attendance system is an automated solution that uses facial recognition technology to identify individuals and record their attendance. By capturing unique facial features, it streamlines the attendance process in various settings, such as workplaces, schools, and events. This system enhances accuracy, reduces errors, and provides real-time tracking of attendance. Key Features of Face Recognition Attendance Systems Biometric Identification: Utilizes unique facial characteristics for identification, making it more secure than traditional methods like passwords. Automated Process: Captures images of individuals as they clock in or out, automatically comparing them to a pre-existing database of facial images. High Accuracy: Advanced algorithms ensure high accuracy in matching faces, even under varying conditions such as different lighting or angles. Real-Time Monitoring: Provides instant feedback on attendance, allowing for immediate identification of any discrepancies or unauthorized access. Integration with HR Systems: Can be integrated with existing HR management systems to streamline payroll and attendance tracking, reducing administrative workload. User -Friendly Interface: Designed for ease of use, allowing employees to clock in and out quickly without extensive training. Benefits of Implementing Face Recognition Attendance Systems Reduced Time Theft: Minimizes issues like buddy punching, where one employee clocks in for another, thus protecting company resources. Enhanced Security: Provides an additional layer of security by ensuring that only authorized personnel can access certain areas or clock in. Improved Accuracy: Reduces human error in attendance tracking, ensuring that payroll is based on accurate data. Cost-Effective: While initial setup may require investment, the long-term savings from reduced time theft and administrative costs can be significant. Scalability: Suitable for various organizations, from small businesses to large enterprises, adapting to the specific needs of each. Challenges and Considerations Privacy Concerns: The use of biometric data raises privacy issues that must be addressed, including obtaining consent from employees. Technical Limitations: Systems may struggle with false positives or negatives, particularly in cases of similar-looking individuals or changes in appearance. Maintenance and Updates: Regular updates and maintenance are necessary to ensure the system remains effective and secure. Initial Costs: The upfront investment for hardware and software can be a barrier for some organizations, though many find the long-term benefits outweigh these costs. In summary, a face recognition attendance system offers a modern, efficient solution for tracking attendance, enhancing security, and improving overall operational efficiency in various environments.

Foreigners Authentication Based on Multi-Biometric System for IraqA. Shamel

criminal face identification system.pptxMaSameer4

IRJET - Facial Recognition based Attendance System with LBPHIRJET Journal

This document presents a facial recognition based attendance system using LBPH (Local Binary Pattern Histograms). It begins with an abstract describing the system which takes student attendance using facial identification from classroom camera images. It then discusses related work in attendance and face recognition systems. The proposed system workflow is described involving face detection, feature extraction using LBPH, template matching, and attendance recording. Experimental results demonstrate the system's ability to detect multiple faces and record attendance accurately in an Excel sheet with date/time. The conclusion discusses how the system reduces human effort for attendance and increases learning time compared to traditional methods.

Hybrid Domain based Face Recognition using DWT, FFT and Compressed CLBPCSCJournals

The characteristics of human body parts and behaviour are measured with biometrics, which are used to authenticate a person. In this paper, we propose Hybrid Domain based Face Recognition using DWT, FFT and Compressed CLBP. The face images are preprocessed to enhance sharpness of images using Discrete Wavelet Transform (DWT) and Laplacian filter. The Compound Local Binary Pattern (CLBP) is applied on sharpened preprocessed face image to compute magnitude and sign components. The histogram is applied on CLBP components to compress number of features. The Fast Fourier Transformation (FFT) is applied on preprocessed image and compute magnitudes. The histogram features and FFT magnitude features are fused to generate final feature. The Euclidian Distance (ED) is used to compare final features of test face images with data base face images to compute performance parameters. It is observed that the percentage recognition rate is high in the case of proposed algorithm compared to existing algorithms.

IRJET- A Review on Face Recognition using Local Binary Pattern AlgorithmIRJET Journal

This document summarizes research on face recognition using the Local Binary Pattern (LBP) algorithm. It begins with an abstract describing biometric identification and how face recognition has become popular in computer vision applications. It then provides details on LBP, describing it as a visual descriptor used for classification. The document reviews how LBP extracts texture features from faces for recognition purposes. It outlines the face recognition process, including detection, feature extraction using LBP, and matching features to a gallery for identification.

Face recognition technologyranjit banshpal

Face recognition technology uses machine learning algorithms to identify or verify a person's identity from digital images or video frames. The process involves detecting faces, applying preprocessing techniques like filtering and scaling, training classifiers using labeled face images, and then classifying new faces. Common machine learning algorithms used include K-nearest neighbors, naive Bayes, decision trees, and locally weighted learning. The proposed system detects faces, builds a tabular dataset from pixel values, trains classifiers, and evaluates performance on a test set. Software applies techniques like detection, alignment, normalization, and matching to encode faces for comparison. Face recognition has advantages like convenience and low cost, and applications in security, banking, and more.

Asistencia | Live Face Prediction | Python Naomi Kulkarni

Face recognition using laplacian facesPulkiŧ Sharma

The document proposes a face recognition method called Laplacianface that uses Locality Preserving Projection (LPP) to map face images into a face subspace. Existing methods like PCA and LDA have disadvantages like less accuracy and not dealing with manifold structure. LPP is presented as a general method for manifold learning that overcomes these issues. The proposed system applies LPP to reduce faces to lower dimensions for recognition. Modules include image manipulation, testing, and form design for entering, identifying, and matching faces. The system could benefit applications like access for the visually impaired.

Stereo matching for 2d face recognitionstudent

This document discusses a novel approach to face recognition using stereo matching algorithms. It begins by introducing face recognition and its challenges, such as handling pose and illumination variations. It then describes the key components of the face recognition system, including detecting facial landmarks, generating a gallery image, and calculating matching costs to identify similarities between images. The document focuses on a stereo matching approach that supports good correspondence between images and maximizes a cost function. It evaluates the performance of this method on real images, finding a recognition rate of 98.27% compared to other approaches.

Final year pptShruti Chandra

This document summarizes a student project on face detection and recognition. The project used OpenCV with Python to detect faces in images and video in real-time. It extracts Haar features and compares them to a training database to recognize faces. The system was able to identify multiple faces with reasonable accuracy, though performance decreased with head tilts or low image quality. Future work could improve robustness to disguises and add emotion or gender analysis.

AN EFFICIENT FACE RECOGNITION EMPLOYING SVM AND BU-LDPIRJET Journal

The document presents a study on an efficient face recognition method employing support vector machines (SVM) and biomimetic uncorrelated local difference projection (BU-LDP). The study proposes using BU-LDP, which is based on uncorrelated local projection but uses a different neighborhood coefficient calculation approach inspired by human perception. Experimental results on several datasets show that BU-LDP and its kernel variant KBU-LDP outperform state-of-the-art methods for face recognition. Future work will focus on addressing the "one sample problem" and applying the approach to unlabeled data.

Face Recongnition using Machine Learningroysarthak272002

This document discusses facial recognition techniques including geometric, eigenfaces, fisherfaces, local binary patterns, active appearance models, 3D shape models, and convolutional neural networks. It outlines the goals of implementing a real-time facial detection and recognition project using a laptop webcam. The methodology that will be used is Dlib's deep learning-based facial recognition which has an accuracy of 99.38% and is easy to implement using its face recognition Python module.

Local directional number pattern for face analysis face and expression recogn...IEEEFINALYEARPROJECTS

Computer Vision - Real Time Face Recognition using Open CV and PythonAkash Satamkar

EE-2018-1303261-1.pdfUmarDrazKhan2

IRJET- DigiyathraIRJET Journal

Face Identification for Humanoid Robotthomaswangxin

Face Recognition System Using Local Ternary Pattern and Signed Number Multipl...inventionjournals

Project presentation by Debendra AdhikariDEBENDRA ADHIKARI

SYNOPSIS on face recognition attendance system softwarezalam5577

Foreigners Authentication Based on Multi-Biometric System for IraqA. Shamel

criminal face identification system.pptxMaSameer4

IRJET - Facial Recognition based Attendance System with LBPHIRJET Journal

Hybrid Domain based Face Recognition using DWT, FFT and Compressed CLBPCSCJournals

IRJET- A Review on Face Recognition using Local Binary Pattern AlgorithmIRJET Journal

Face recognition technologyranjit banshpal

Asistencia | Live Face Prediction | Python Naomi Kulkarni

Face recognition using laplacian facesPulkiŧ Sharma

Stereo matching for 2d face recognitionstudent

Final year pptShruti Chandra

AN EFFICIENT FACE RECOGNITION EMPLOYING SVM AND BU-LDPIRJET Journal

Face Recongnition using Machine Learningroysarthak272002

Local directional number pattern for face analysis face and expression recogn...IEEEFINALYEARPROJECTS

Computer Vision - Real Time Face Recognition using Open CV and PythonAkash Satamkar

More from Marwan H. Noman (9)

Decision Making Framework Supported by Knowledge Management ActivitiesMarwan H. Noman

HP Challenges, Organizational LearningMarwan H. Noman

HP faced challenges in managing its talent and distributing product information globally. For talent management, HP aligned its strategy with business goals, had managers assess employees, and provided development programs involving mentoring and education. This helped retain young talent. For content management, HP reconstructed its supply chain and treated content globalization as a business process. It established processes to provision localized content consistently across regions through subscription and delivery mechanisms. These solutions helped HP better manage its talent and distribute product information worldwide.

Organization DesignMarwan H. Noman

The document provides an overview of organizational design and structure. It discusses key concepts such as departmentalization, establishing reporting relationships, allocating authority, and the basic forms of organizational design. Specifically, it addresses: 1) The importance of organizational design in enabling groups to coordinate resources and activities to produce value. Different approaches to departmentalization and their advantages and disadvantages are examined. 2) The need to establish clear reporting relationships to clarify lines of authority and responsibility within the organizational structure. 3) How authority is allocated through concepts like line and staff authority, and functional authority. Line authority flows down the chain of command while staff authority allows advising those with line authority.

Starbucks, Strategic KM Marwan H. Noman

The document provides an overview of Starbucks, including its logo, profile, structure, marketing activities, operations, use of IT, financial report, Porter's forces analysis, SWOT analysis, and recommendations. It discusses how Starbucks was founded in 1971 and now has over 13,100 stores worldwide. The logo is derived from Moby Dick and depicts a siren. Starbucks has a functional structure without a formal hierarchy and emphasizes employee input.

Members and Leaders in Groupo CommunicationMarwan H. Noman

This document discusses group communication, including member roles, leadership styles, and groupthink. It describes the three main types of member roles as task roles, building/maintenance roles, and individual roles. Three leadership styles are discussed: laissez-faire, democratic, and authoritarian. The document also defines groupthink as a way of thinking that prioritizes agreement over logical evaluation, and lists its major symptoms such as rationalizing warnings and believing the group is always right.

Organization Structure and DesignMarwan H. Noman

The document discusses various concepts related to organizational structure and design including departmentalization, establishing reporting relationships, allocating authority, coordination activities, and basic forms of organizational design. It provides examples of different departmentalization approaches like functional, team-based, matrix, and network. It also outlines principles of organizational design such as division of labor, unity of command, and spans of control. Current issues organizations face include adapting to a changing environment and addressing workforce diversity and ethics.

Starbucks - Strateic Knowledge Management Marwan H. Noman

KM TechnologiesMarwan H. Noman

This document discusses technologies for knowledge management applications. It states that technology is one of the four pillars of knowledge management, along with people, processes, and organizational structure. It supports and enables knowledge management strategies and operations. While technology alone does not create an effective knowledge management system, it can facilitate connections and communications. The main role of technology in knowledge management is to help people share knowledge through common storage to achieve economic reuse of knowledge. It discusses how knowledge management technology can capture, organize, retrieve, distribute, and maintain knowledge. Various knowledge management tools are also outlined.

Decision Making framework supported by KM activitiesMarwan H. Noman

The document discusses decision making methods and how knowledge management can improve decision making. It outlines the scope of the study, which will examine decision making in different organizations, industries, and cultures. The research questions will focus on the relationship between knowledge management and decision making and the impact of KM strategies on decision making. The expected conclusion is that a decision making framework supported by knowledge management activities can enhance decision making processes in organizations.

Decision Making Framework Supported by Knowledge Management ActivitiesMarwan H. Noman

HP Challenges, Organizational LearningMarwan H. Noman

Organization DesignMarwan H. Noman

Starbucks, Strategic KM Marwan H. Noman

Members and Leaders in Groupo CommunicationMarwan H. Noman

Organization Structure and DesignMarwan H. Noman

Starbucks - Strateic Knowledge Management Marwan H. Noman

KM TechnologiesMarwan H. Noman

Decision Making framework supported by KM activitiesMarwan H. Noman

face recognition system using LBP

1. Final Year Project Face Recognition Using Local Features Marwan Noman 1051108336

2. Implementation Plan Milestones of Phase II:

3. Solution - Enrollment Process: 1. The images were enrolled by applying LBP function. 2. The histograms of these images will be stored as vectors in a matrix. 3. The distance function will compare two matrixes from the same dimensionality to get matching image.

4. Solution (2) - Enrollment Process:

5. Solution (3) Feature Extraction Algorithm:

6. Solution (4) -Matching Algorithm:

7. Implementation Process Extracted Histogram:

8. Implementation Process (2) The LBP is used to extract the local important features of the images. Hence, it will convert the result to histogram values. These values will be stored in a matrix as values.

9. Implementation Process (3) - Enrollment and Recognition processes : - There are around 96 images of different subjects. - There are 12 different subjects. Each subject has 8 different images. - An image will be used of each subject for recognition purposes.

10. Implementation Process (4) - Basic Design:

11. Implementation Process (5) - Finalized Design:

12. Implementation Process (6) - Loading the image :

13. Implementation Process (7) - Extracting the image:

14. Implementation Process (8) - Scanning the image:

15. Testing - Low quality images:

16. Testing (2) - False Matching:

17. Testing (3) - Positive Matching:

18. Conclusion - Great experience of Face Recognition systems . - Achievement of a good level of understanding LBP algorithm, and it was a good opportunity for me to distinguish between local features and global features of images . - One of the problems was faced during the testing, is image quality.

19. Conclusion (2) - Euclidean Distance was the solution by finding the distance between histograms of the original image and the enrolled images . - By the minimum distance between the histograms, it will find the most matching image of the original one. - It is compulsory to crop bad quality images .

20. The End

face recognition system using LBP

Recommended

More Related Content

What's hot (20)

Viewers also liked (20)

Similar to face recognition system using LBP (20)

More from Marwan H. Noman (9)

face recognition system using LBP