Principal Component Analysis(PCA) with Back Propogation Neural Network(BPNN) for Face Recognition System
Sneha P. Wandale · 2013
Faces represent complex, multidimensional, meaningful visual stimuli and developing a model for face recognition is difficult. We present a neural network solution which comprises of identifying a face image from the face’s unique features. Face detection and recognition has many applications in a variety of fields such as authentication, security, video surveillance and human interaction systems. Face recognition can be applied for a wide variety of problems like image and film processing, human-computer interaction, criminal identification etc. This research deals with the implementation of face recognition system using neural network. Importance of face recognition system has speed up in the last few decades. The problem in face recognition is to find the best match of an unknown image against a database of face models or to determine whether it does not match any of them well. In many face recognition systems the important part is face detection. The task of detecting face is complex due to its variability present across human faces including color, pose, expression, position and orientation. So using various modeling techniques it is convenient to recognize various facial expressions. 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 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 In the field of image processing it is very interesting to recognize the human gesture by observing the different movement of eyes, mouth, nose, etc.