Author Topic: Face Recognition System Based on Principal Component Analysis (PCA) with Back Pr  (Read 2488 times)

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Author : Mohammod Abul Kashem, Md. Nasim Akhter, Shamim Ahmed, and Md. Mahbub Alam
International Journal of Scientific & Engineering Research Volume 2, Issue 6, June-2011
ISSN 2229-5518
Download Full Paper : PDF

Abstract— Face recognition has received substantial attention from researches in biometrics, pattern recognition field and computer vision communities. Face recognition can be applied in Security measure at Air ports, Passport verification, Criminals list verification in police department, Visa processing , Verification of Electoral identification and Card Security measure at ATM’s. In this paper, a face recognition system for personal identification and verification using Principal Component Analysis (PCA) with Back Propagation Neural Networks (BPNN) is proposed. This system  consists on three basic steps which are automatically detect human face image using BPNN, the various facial features extraction, and face recognition are performed based on Principal Component Analysis (PCA) with BPNN. The dimensionality of face image is reduced by the PCA and the recognition is done by the BPNN for efficient and robust face recognition. In this paper also focuses on the face database with different sources of variations, especially Pose, Expression, Accessories, Lighting  and backgrounds would be used to advance the state-of-the-art face recognition technologies aiming at practical applications.
Index Terms— Face Detection, Facial Features Extraction, Face Database, Face Recognition, Increase Acceptance ratio and Reduce Execution Time.

1   INTRODUCTION
WITHIN computer vision, face recognition has become increasingly relevant in today’s society. The recent interest in face recognition can be attributed to the increase of commercial interest and the development of feasible technologies to support the development of face recogni-tion. Major areas of commercial interest include biometrics, law enforcement and surveillance, smart cards, and access control. Unlike other forms of identification such as fingerprint analysis and iris scans, face recognition is user-friendly and non-intrusive. Possible scenarios of face recognition include: identification at front door for home security, recognition at ATM or in conjunction with a smart card for authentication, video surveillance for security. With the advent of electronic medium, especially computer, society is increasingly dependent on computer for processing, storage and transmission of information. Computer plays an important role in every parts of today life and society in modern civilization. With increasing technology, man becomes involved with computer as the leader of this technological age and the technological revolution has taken place all over the world based on it. It has opened a new age for humankind to enter into a new world, commonly known as the technological world. Computer vision is a part of every day life. One of the most important goals of computer vision is to achieve visual recognition ability comparable to that of human [1],[2],[3].
     Face recognition has received substantial attention from researches in biometrics, pattern recognition field and computer vision communities. In this paper we proposed a computational model of face detection and recognition, which is fast, reasonably simple, and accurate in constrained environments such as an office or a household. Face recognition using Eigen faces has been shown to be accurate and fast. When BPNN technique is combined with PCA, non-linear face images can be recognized easily.

2   OUTLINE OF THE SYSTEM
In this papers to design and implementation of the Face Recognition System (FRS) can be subdivided into three main parts. The first part is face detection-automatically face detection can be accomplished by using neural net-works back propagation. The second part is to perform various facial features extraction from face image using digital image processing and Principal Component Analysis (PCA). And the third part consists of the artificial intelligence (face recognition) which is accomplished by Back Propagation Neural Network (BPNN).
The first part is the Neural Network-based Face Detection described in [4]. The basic goal is to study, implement, train and test the Neural Network-based machine learning system. Given as input an arbitrary image, which could be a digitized video signal or a scanned photograph, determine whether or not there are any human faces in the image, and if there are, return an encoding of the location and spatial extent of each human face in the image. The first stage in face detection is to perform skin detection. Skin detection can be performed in a number of color models. To name a few are RGB, YCbCr, HSV, YIQ, YUV, CIE, XYZ, etc. An efficient skin detection algorithm is one which should be able to cover all the skin colors like black, brown, white, etc. and should account for varying lighting conditions. Experiments were performed in YIQ and YCbCr color models to find out the robust skin color model. This part consists of YIQ and YCbCr color model, skin detection, blob detection, smooth the face, image scaling.
Fig: 3.  (a) skin detection, and (b) face detection.    The second part is to perform various facial features extraction from face image using digital image processing and Principal Component Analysis (PCA) and the Back Propagation Neural Network (BPNN). We separately used iris recogni-tion for facial feature extraction. Facial feature extraction consists in localizing the most characteristic face compo-nents (eyes, nose, mouth, etc.) within images that depict human faces. This step is essential for the initialization of many face processing techniques like face tracking, facial expression recognition or face recognition. Among these, face recognition is a lively research area where it has been made a great effort in the last years to design and compare different techniques. The second part consists of face land-marks, iris recognition, fiducial points.
    The third part consists of the artificial intelligence (face recognition) which is accomplished by Back Propagation Neural Network (BPNN). This paper gives a Neural and PCA based algorithm for efficient and robust face recogni-tion. This is based on principal component-analysis (PCA) technique, which is used to simplify a dataset into lower dimension while retaining the characteristics of dataset. Pre-processing, Principal component analysis and Back Propagation Neural Algorithm are the major implementations of this paper.
   This papers also focuses on the face database with different sources of variations, especially Pose, Expression, Accessories, and Lighting would be used to advance the state-of-the-art face recognition technologies aiming at practical applications especially for the oriental.

3   FACE DETECTION
The face detection can be perform by given as input an arbitrary image, which could be a digitized video signal or a scanned photograph, determine whether or not there are any human faces in the image, and if there are, return an encoding of the location and spatial extent of each human face in the image[5].

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