face recognition system using LBP
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1) The document discusses a final year project on face recognition using local features such as Gabor and LBP. 2) It reviews literature on biometrics and common face recognition algorithms like PCA, LDA, and LBP. 3) The methodology section explains how LBP works by comparing pixel values to label images and extracting histograms to represent facial features.
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face recognition system using LBP
- 1. Final Year Project Face Recognition Using Local Features
- 2. Introduction - What is Face Recognition? - Using Global Features: Example: PCA and LDA - Using Local Features: Example: Gabor and LBP - Goal of Face Recognition.
- 3. Introduction - Objectives of Research: 1- To Study Face Recognition Systems. 2- To design and develop Face Recognition Systems. 3- To implement Face Recognition System using enhanced local features.
- 4. Literature Review: Biometrics - Biometrics:
- 5. Literature Review: Biometrics(2) - Biometrics: From Greek words. - Bio= life , metrics= to measure . - Biometrics : identify and verify a person based on their Psychological and behavioral characteristics. - This first type of biometrics was fingerprint.
- 6. Literature Review: Face Recognition - Face Recognition: - Verification: (1:1) - Required Unique ID and Biometric Sample. - It will compare the biometric template (sample) with the one it has on records . - “Match” or “Not Match”. - Identification: (1:M) - Required Only Biometric Sample. - It will compare the biometric template (sample) using a smart algorithm, with each one of the records in a file. - positive ID of specific Identity given by its unique User ID.
- 7. Literature Review: Face Recognition Features
- 8. Literature Review: Face Recognition Features(2) - Face Recognition Features: - Global Features: - Focus on the Whole Entire Image. - Less Accuracy. - Local Features: - Focus on the local features of the face, which help to identify and verify the persons using the unique details in the face. - More Accuracy.
- 9. Literature Review: Face Recognition Algorithms - Face Recognition Algorithms: - PCA (Principle Component Analysis): - Works on Global Features. - It is the most famous. - It is called Eigenface. - It tries to find a lower dimensional subspace to describe the original face space. - there are statistical equations will be used to get the required image
- 10. Literature Review: Face Recognition Algorithms(2) - ICA (Independence Component Analysis): - It is a statistical signal processing technique. - It is a special case of redundancy reduction technique and it represents the data in terms of statistically independent variables. - Its goal is to minimize the statistical dependence between basic vectors. - Provides powerful data representation than PCA.
- 11. Literature Review: Face Recognition Algorithms(3) - LDA (Linear Discriminate Analysis): - Is a dimensionality reduction technique. - It searches for those vectors in the underlying space that best discriminate among classes, - Its main idea is to find a linear transformation such that feature clusters are most separable, which can be achieved through scatter matrix analysis.
- 12. Literature Review: Face Recognition Algorithms(4) - LBP (Local Binary Pattern): - It works on Local Features - LBP operator: summarizes the local special structure of an image. - LBP is defined as an ordered set of binary comparisons of pixel intensities between the center pixel and its eight surrounding pixels.
- 13. Literature Review: Face Recognition Algorithms(5) - LBP (Local Binary Pattern): . - decimal form of the resulting 8-bit word (LBP code) can be expressed as follows:
- 14. Literature Review: Face Recognition Algorithms(6) - LBP (Local Binary Pattern): - We can also do this comparison by applying the following formula:
- 15. Methodology: LBP - LBP (Local Binary Pattern): - It is used to determine the local features in the face. - It works by using basic LBP operator. - in a matrix originally of size 3×3, the values are compared by the value of the centre pixel, then binary pattern code is produced. - The LBP code is obtained by converting the binary code into decimal one.
- 16. Methodology: LBP Histograms - LBP Histograms (Local Binary Pattern): - Each pixel of an image is labeled with an LBP code . - First it will divide the image to several blocks. - Then it will start calculating the LBP histogram for each block. - after that it will combine every LBP histogram for that image - then you will get all the LBP histograms into one vector.
- 17. Methodology: LBP Histograms(2)
- 18. Methodology: LBP Flowchart
- 19. Methodology: LBP Flowchart(2) - LBP Process Flowchart: - Capture an image then store it. - The process will divide the image to several blocks. - Histograms will be calculated for each block, then a histograms will be concentrated into a single vector. - As a result, the facial recognition is represented by LBP and the shape of the face is obtained by concentration of different local histograms.
- 20. Proposed Solution: LBP Analysis - LBP Analysis: -
- 21. Proposed Solution: LBP Analysis(2) - First, the image will be divided to several blocks; each block is as a matrix of type 3 X 3. - Then, in each matrix the system will be comparing the center pixel of the block with the other pixels. - If Center pixel >= other pixel = 1, else = 0. - Getting the binary number of each block. - Converting to decimal number.
- 22. Proposed Solution: Interface Design - Main page:
- 23. Proposed Solution: Interface Design(2) - Example of images Database:
- 24. Proposed Solution: Interface Design(3) - Scanning Process:
- 25. Proposed Solution: Interface Design(4) - Matched Image:
- 26. Proposed Solution: Interface Design(5) - Image with no matches:
- 27. Proposed Solution: Implementation Plan (Phase II) - Getting more information about MatLab. - Studying tutorials about MatLab. - Improving the Interim report details. - Training and testing the system. - Overcoming the problems. - Understanding and analyzing the idea of Local Binary Patterns. - More Research about Face Recognition based on Local Features.
- 28. Conclusion - LBP (Local Binary Patterns) is used to extract the local features in the face and match it with the most similar face image in the database. - LBP is a method that works by dividing the face image to several blocks. - In the matrix we compare the pixels with the center pixel . - at the end we will get a binary number which will be converted into decimal format. - will be combined together under one vector which will help to recognize the face.
- 29. The End