IJIRST 窶的nternational Journal for Innovative Research in Science & Technology| Volume 1 | Issue 6 | November 2014 ISSN (online): 2349-6010
Face Recognition using Eigenfaces for Android Application Shrutika Yawale Student Department of Computer Engineering Kirodimal Institute of Technology Raigarh (C.G.) INDIA
Mayuri Patil Student Department of Computer Engineering Kirodimal Institute of Technology Raigarh (C.G.) INDIA
Shweta Pinjarkar Student Department of Computer Engineering Kirodimal Institute of Technology Raigarh (C.G.) INDIA
Reshma Adagale Faculty Department of Computer Engineering Kirodimal Institute of Technology Raigarh (C.G.) INDIA
Abstract Face recognition is the technique which can be applied to the wide variety of problems like image and film processing, human computer interaction, criminal identification etc. This has motivated researchers to develop computational models to identify the faces, which are easy and simple to implement. In this, demonstrates the face recognition system in android device using eigenfaces. The implemented system is able to perform real-time face detection, face recognition and can give feedback giving a window with the subject's info from database and sending an e-mail notification to interested institutions using android application. Keywords: Feature vector, eigenfaces, eigenvalues, eigenvector, face recognition, real-time. _______________________________________________________________________________________________________
I. INTRODUCTION Face recognition uses eigenfaces algorithm.This feature can be used in Android application.Face recognition has become an important issue in many applications such as security systems, biometric authentication, human-computer interaction, criminal identification. Even the ability to merely detect faces, as opposed to recognizing them, can be important. Face recognition has several advantages over other biometric technologies: it is natural, non-intrusive and easy to use. A face recognition system is expected to identify faces present in images and video automatically. It can operate in either or both of two modes: a) face verification (or authentica-tion), and b) face identification (or recognition). A. Face verification: (Am I whom I claim I am?) : This mode is used when the person provides an alleged identity. The system then performs a one-to-one search, comparing the captured biometric characteristics with the biometric template stored in the database. If a match is made the identity of the person is verified [1]. B. Face identification: (Who am I?) : This mode is used when the identity of the individual is not known in advance. The entire template database is then search for a match to the individual concerned, in a one-to-many search. If a match is made, the individual is identified [1].
II. FACE RECOGNITION USING EIGENFACES The goal of a face recognition system is to discriminate input signals (image data) into several classes (persons), being important for a wide variety of problems like image and film processing, human-computer interaction, criminal identification and others. The inputs signals can be highly noisy because of different lightning conditions, pose, expression, hair窶ｦ. Nevertheless, the input signals are not completely random and even more, there are patterns present in each input signal. One can observe in all input images common objects like: eyes, mouth, nose and relative distances between these objects. These common features are called eigenfaces [11] in the facial recognition domain (or principal components generally). They can be extracted out of the original image data through a mathematical technique called Principal Component Analysis (PCA).
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Face Recognition using Eigenfaces for Android Application (IJIRST/ Volume 1 / Issue 6 / 032)
Fig 3.1 Block Diagram
III. ALGORITHM (1) The first step is to obtain a set S with M face images. In our example M = 25 as shown at the beginning of the tutorial. Each image is transformed into a vector of size N and placed into the set.
(2) After
you
have
obtained
your
set,
you
will
obtain
the
mean
image
Ψ
(3) Then you will find the difference Φ between the input image and the mean image
(4) Next we seek a set of M orthonormal vectors, un, which best describes the distribution of the data. The kth vector, uk, is chosen such that
is a maximum, subject to
Note:
uk
and
λk
are
the
eigenvectors
and
eigenvalues
of
the
covariance
matrix
C
(5) We obtain the covariance matrix C in the following manner
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Face Recognition using Eigenfaces for Android Application (IJIRST/ Volume 1 / Issue 6 / 032)
(6) AT
(7) Once we have found the eigenvectors, vl, ul
These are the eigenfaces of our set of original images.
IV. RECOGNITION PROCEDURE (1) A new face is transformed into its eigenface components. First we compare our input image with our mean image and multiply their difference with each eigenvector of the L matrix. Each value would represent a weight and would be saved on a vector Ω.
(2) We now determine which face class provides the best description for the input image. This is done by minimizing the Euclidean distance
(3) The input face is consider to belong to a class if εk is bellow an established threshold θε. Then the face image is considered to be a known face. If the difference is above the given threshold, but bellow a second threshold, the image can be determined as a unknown face. If the input image is above these two thresholds, the image is determined NOT to be a face. (4) If the image is found to be an unknown face, you could decide whether or not you want to add the image to your training set for future recognitions. You would have to repeat steps 1 trough 7 to incorporate this new face image.
V. CONCLUSION The Eigenface approach for Face Recognition process is fast and simple which works wellunder constrained environment. It is one of thebest practical solutions for the problem of facerecognition. Many applications which require face recognition do not require perfect identification but just low error rate. So instead of searching large database of faces, it is better to give small set of likely matches. By using Eigenface approach, this small set of likely matches for given images can be easily obtained
REFERENCES [1] [2] [3] [4]
M. Turk and A. Pentland ,―Eigenfaces for recognition‖, Journal of Cognitive Neuroscience, vol.3, No.1, 1991. M. Turk and A. Pentland, ―Face recognition using eigenfaces‖, Proc. IEEE Conf. on Computer Vision and Pattern Recognition, pages 586-591, 1991. A. Pentland and T. Choudhury, ―Face recognition for smart environments‖, Computer, Vol.33 Iss.2, Feb. 2000. [4] R. Brunelli and T. Poggio, ―Face recognition: Features versus Templates‖, IEEE Trans. Pattern Analysis and Machine Intelligence, 15(10): 1042-1052, 1993. R. A. Fisher, “The use of multiple measures in taxonomic problems,”Ann. Eugenics, II, vol. 7, pp. 179–188, 1936.
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