Face Recognition Using Eigen Faces Algorithm

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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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