Human Face Detection and Tracking for Age Rank, Weight and Gender Estimation based on Face Images utilizing Raspberry Pi Processor

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056
Volume: 04 Issue: 02 | Feb -2017 www.irjet.net p-ISSN: 2395-0072
© 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal | Page 185
Human Face Detection and Tracking for Age Rank, Weight and Gender
Estimation based on Face Images utilizing Raspberry Pi Processor
Mr. Sarang C. Zamwar, Dr. S. A. Ladhake, Mr. U. S. Ghate
PG Student [Digital Electronics], Department of Electronics and Telecommunication, Sipna College of engineering
and technology, Amravati (M.S.), India
Principal, Sipna College of engineering and technology, Amravati (M.S.), India
Assistant Professor, Department of Electronics and Telecommunication Sipna College of engineering and
technology, Amravati (M.S.), India
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Abstract - This paper describes the method for real time
human face detection and tracking for age rank, weight and
gender estimation. Face detection is involved with finding
whether or no longer there are any faces in a given imageand,
if present, track the face and returns the face region with
content of each face. Here describes a easy and convenient
hardware implementation of face detection procedure
utilizing Raspberry Pi, which itself is a minicomputer of a
credit card size. This paper presents a cost-sensitive ordinal
hyperplanes ranking algorithm for human age evaluation
based on face images. Two main components for building an
effective age estimator are facial feature extraction and
estimator learning. Using feature extraction and comparing
with our input data in which we have different age group face
images with weight is specified according to that we also
specify weight. In this article we present a novel multimodal
gender estimation, which effectively integrates the head as
well as mouth motion information with facial appearance by
taking advantage of a unified probabilistic framework. Facial
appearance as well as head and mouth motion possess a
potentially relevant discriminatory power, and that the
integration of different sources of biometric data from video
sequences is the key approach to develop more precise and
reliable realization systems.
Key Words: Age rank, human face detection, Ordinal
hyperplanes ranking, Raspberry pi, Tracking, Unified
probabilistic framework, Weight
1. INTRODUCTION
Most face detection algorithms are designed in the
software domain and have a high recognition rate, but they
often require several seconds to detect faces in a single
image, a processing speed that is insufficient for real-time
applications. A simple and easy hardwareimplementation of
face detection system using Raspberry Pi, which itself is a
minicomputer of a credit card size and is of a very low price.
Automatic age estimation, which involves evaluating a
person’s exact age or age-group and weight estimation, is a
crucial topic in human face image understanding. Aeffective
gender classification process can improve the performance
of many different applications, including person recognition
and smart human-computer interfaces. Here use of
Raspberry Pi board as a platform for this process. Camera Pi
is an excellent add-on for Raspberry Pi, to take pictures and
record quality videos, with the possibility to apply a
considerable range of configurations and effects.
For real time and from specific image face detection, i.e.
Object detection, is done and the proposed system is tested
across various standard face databases, with and without
noise and blurring effects. Efficiency of the system is
examine by calculating the Face detection rateforeachof the
database. The results disclose that the proposed system can
be used for face recognition evenfromlow qualityimageand
shows excellent performance efficiency. Automatic age
estimation, which involves evaluatinga person’sexactageor
age-group, is a crucial topic in human face image
understanding. The task of estimating exact human age
adopts a dense representation of the age labels (e.g., from 0
to 80), and the task of age-group estimation divides the
labels only into rough groups (e.g., elder, adult, and
teenage/children). In this paper, we focus on the setting of
the former task that can be applicable to more general
situations. Nevertheless, the proposed method can be used
for age-group estimation as well. Two main components for
building an effective age estimator are facial feature
extraction and estimator learning.
Recognizing human gender is important since people
respond differently according to gender. In addition, A
effective gender classification process can improve the
performance of many different applications ,including
person recognition and smart human-computer interfaces.
In this article, we presents the problem of automatic gender
identification by exploiting the physiological and aspects of
the face at the same time, we explore the possibility of using
head motion, mouth motion and facial appearance in a
gender identification scenario. Hence,proposea multimodal
recognition approach that integrates the temporal and
spatial information of the face through a probabilistic
framework.

The advantages of this system is real time face detection
and tracking is possible, The raspberry Pi processorisoflow
cost, Execution speed is very fast, More than one face can
also be detected using this system at a time. Theefficiency of
the system was analyzed in terms of face detection rate. The
analysis revealed that the present system shows excellent
performance efficiency and can be used for face detection
even from poor quality images.
2. LITERATURE REVIEW
Methodology for face recognition based on information
theory approach of coding and decoding the face image is
discussed in [Sarala A. Dabhade & Mrunal S. Bewoor, 2012]
[1]. Proposed methodology isconnectionoftwostages–Face
detectionusingHaarBasedCascadeclassifierandrecognition
using Principle Component analysis. Various face detection
andrecognitionmethodshavebeenevaluated[FaizanAhmad
et al., 2013] [2] and also solution for image detection and
recognition is proposed as an initial step for video
surveillance. Implementation of face recognition using
principal component analysis using 4 distance classifiers is
proposed in [Hussein Rady, 2011] [3]. A system that uses
different distance measures for each image will perform
better than a system that only uses one. The experiment
show that PCA gave better results with Euclidian distance
classifier and the squared Euclidian distance classifier than
the City Block distance classifier, which gives better results
than the squared Chebyshev distance classifier. A structural
face construction and detection system is presented in
[Sankarakumar et al., 2013] [4]. The proposed system
consists the different lightning, rotated facial image, skin
color etc.
Lanitis et al. [5] proposed the first approach applying AAM
to ageestimation,whichextractscraniofacialgrowthandskin
aging during childhood and adulthood. Different classifiers
(includingshortest-distanceclassifier,quadraticfunctionand
neural networks) are compared when AAM is employed as
the feature representation. The approach also differentiated
between 1) age-specific estimation, which is based on the
assumption that the aging process is identical for everyone;
and 2) appearance-specific estimation, which follows the
assumption that people who look similartendtohavesimilar
aging processes. Subsequently,apersonalizedageestimation
used in the specialty of aging processes is then introduced to
cluster similar faces before classification. In addition,Genget
al. [6] modeled the aging process with AAM based on a
sequence of age-ascending face images for the same
individual. Hence, different aging models can be learnt for
different persons. More specifically,Gengetal.[7]introduced
a personalized age estimation method that describes the
long-term aging subspace of a person, called Aging pattern
Subspace (AGES). AGES estimates his/her age by projecting
the query face into the aging subspace that best reconstruct
the face image.
Sun et al. [8] applied principalcomponentanalysis(PCA)to
represent each image as a featurevectorinalowdimensional
space; genetic algorithms (GA) were then employed to select
a subset of features form the low dimensionalrepresentation
that mostly encodes the gender information. Four different
classifierswerecomparedinthisstudy:theBayesiandecision
making, a neural network (NN), support vector machines
(SVM) and a classifier based on linear discriminant analysis
(LDA). Nakano et al. [9] focused on the edge information and
exploited a neural network (NN) classifier for gender
recognition. In particular, they computed the density
histograms of the edge images, which were successively
treated as input features for the NN. Kim et al. [10] base their
gender recognition system on a Gaussian Process Classifier
(GPC). Facial images are first normalized to a standard
dimensions and background and hair information was
removed. Parameters for the GPC are learned using
Expectation Maximization (EM) - Expectation Propagation
(EP) algorithm. Finally GPC is used for classification.
3. PROPOSED WORK
A general block diagram of the system is as shown below :

Working :
Intially image is captured real time using the USB camera.
OpenCV is used at face detection stage. OpenCV (Open
Source Computer Vision) is a library of programming
functions mainly aimed at real-time computer vision. In
simple language it is library used for Image Processing. It is
mainly used to do all the operation related to Images.
Now face detection and extraction algorithm will work i.e.
viola Jones algorithm which uses Haarfeaturebasedcascade
classifiers algorithm for face detection. As long as a face is
detected, a red bounding box is drawn on the face in the
image.
Local binary pattern method which is most successful for
face recognition is used for feature extraction for age
estimation. After that extracted features is given to
convolution neural network (CNN) which is pre-trained
model will find out whether the features extraction of an
image in testing set is matching to thefeature extractedfrom
the training set and gives the estimated age.
Histogram of oriented gradients (HOG) algorithm is used
for gender and weight estimation. HOG algorithm is used for
feature extraction for gender and weight estimation. Now
SVM (support vector machine) will find out whether the
feature extraction of an image in testing set is matching to
the feature extracted from the training set. Finally output
will be displayed on screen.
Proposed system uses different techniques for face
recognition, age estimation, weight estimation and gender
estimation namely
1. Viola Jones Algorithm :
The basic principal of algorithm is todetectthefacesfrom
the given input image. Before this there were so many
images processing approach but all of them were time
consuming due to making the entire image to the fixsizeand
then run the image in the detector. Opposite of this is the
viola Jones algorithm were the detector is rescale and
whatever the size of image would be.
2. Histogram of oriented gradients (HOG) Algorithm :
The histogram of oriented gradients (HOG) is a feature
descriptor used in computer vision and image processing
for the purpose of object detection. The technique counts
occurrences of gradient orientation in localized portions of
an image. This method is similar to that of edge orientation
histograms, scale-invariant feature transform descriptors,
and shape contexts, but differs in that it is computed on a
dense grid of uniformly spaced cells and uses overlapping
local contrast normalization for improved accuracy. It use
for gender estimation procedure.
3. Local binary patterns (LBP) :
LBP is one of the binary patterns which is used for feature
extraction. In this the face image is firstly divided into small
regions from which LBP features are extracted gives the
output in histogram. LBP is used because there are micro
patterns which are invariant of monotonic grey scale
transformation. Combining all this gives the face image. LBP
is widely used in many application due to its high tolerance
against object recognition texture analysis and high
discriminative power.
4. Support vector machines :
In machine learning, support vector machines (SVMs) are
supervised learning models with associated learning
algorithms that analyze data used for classification and
regression analysis. In support vector machine is used to
analyze the complex data and gives the result. SVM is very
useful in finding patterns which are very useful and not
complex.
5. Convolution Neural Network :
In machine learning, a convolution neural network (CNN)
is a type of feed-forward artificial neural network in which
the connectivity pattern between its neurons is inspired by
the organization of the animal visual cortex. Individual
cortical neurons respond to stimuli in a restricted region of
space known as the receptive field. The receptive fields of
different neurons partially overlap such that they tile the
visual field. The response of an individual neuron to stimuli
within its receptive field can be approximated
mathematically by a convolution operation. Convolutional
networks were inspired by biological processes and are
variations ofmultilayerperceptronsdesignedtouseminimal
amounts of preprocessing. They have wide applications in
image and video recognition, recommender systems and
natural language processing.
4. CONCLUSION
This paper proposed a system for face detection, tracking,
age, weight and gender estimation technique. Also, some
popular well-known face detection technique is described.
Face detection techniques have been employed in different
applications such as face recognition, facial feature
extraction. On the basis of this age, weight and gender
estimation will be done using the algorithms mentioned
above.
Face detection and tracking is being challenging for many
researchers with real time Image sensor. With the advance-
ment the real time face detection in remote monitoring is
help for building much efficient application. Moreover such
technology can be useful in tracking the lost object under
dynamic environment. Further enhancement of this work
can be extended with stereo depth analysis of face detection
using two image sensor interfaced with High speed
Processor. The future scope of this is to improve the
database of public where the large public database is
available.

REFERENCES
[1] Sarala A. Dabhade & Mrunal S. Bewoor (2012), “Real
Time Face Detection and RecognitionusingHaar -based
Cascade Classifier and Principal Component Analysis”,
International Journal of Computer Science and
Management Research, Vol. 1, No. 1.
[2] Faizan Ahmad, Aaima Najam & Zeeshan Ahmed (2013),
“Image-based Face Detection and Recognition: State of
the Art”, IJCSI International Journal of Computer Science
Issues, Vol. 9, Issue. 6, No. 1.
[3] Hussein Rady (2011), “Face Recognition using Principle
Component AnalysiswithDifferentDistanceClassifiers”,
IJCSNS International Journal of Computer Science and
Network Security, Vol. 11, No. 10, Pp. 134–144.
[4] S. Sankarakumar, Dr.A. Kumaravel & Dr.S.R. Suresh
(2013), “Face Detection through Fuzzy Grammar”,
International Journal of Advanced Research in Computer
Science and Software Engineering, Vol. 3, No. 2.
[5] A. Lanitis, C. J. Taylor, and T. F. Cootes, “Toward
automatic simulation of aging effects on face images,”
IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 4, pp.
442–455, Apr. 2002.
[6] X. Geng, Z.-H. Zhou, and K. Smith-Miles, “Automatic age
estimation based on facial aging patterns,” IEEE Trans.
Pattern Anal. Mach. Intell., vol. 29, no. 12, pp. 2234–
2240, Dec. 2007.
[7] X. Geng, Z.-H. Zhou, Y. Zhang, G. Li, and H. Dai, “Learning
from facial aging patterns for automatic ageestimation,”
in Proc. 14th Annu.
[8] Sun Z., Bebis G., Yuan X. and Louis S.J., “Genetic feature
subset selection for gender classification: a comparison
study”, in IEEE Proceedings on Applications of Computer
Vision, pag. 165-170, 2002.
[9] Nakano M., Yasukata F. and Fukumi M., “Age and gender
classification from face images using neural networks”,
in Signal and Image Processing, 2004.
[10] Kim H.-C., Kim D., Ghahramani Z. and Bang S.Y.,
“Appearance based gender classification with Gaussian
processes”, in Pattern Recognition Letters, vol. 27, iss. 6,
pag. 618-626, April 2006.
[11] P. Viola and M. Jones. Robust Real-timeObjectDetection.
International Journal of Computer Vision, 57(2):137–
154,2002. 2, 4.
[12] G. Yangand, T. S. Huang, “Human face detection in
complex background,” Pattern Recog., vol. 27, no. 1,
1994, pp. 53–63
BIOGRAPHIES
PG Student [Digital Electronics],
Department of Electronics and
Telecommunication, Sipna College
of engineering and technology,
Amravati(M.S.), India

Human Face Detection and Tracking for Age Rank, Weight and Gender Estimation based on Face Images utilizing Raspberry Pi Processor

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