Principle Component Selection for Face Recognition Using Neural Network
AL-Rafidain Journal of Computer Sciences and Mathematics,
2009, Volume 6, Issue 1, Pages 179-191
AbstractFace Recognition is an emerging field of research with many challenges such as large set of images,. Artificial Neural Network approach is one of the simplest and most efficient method to overcome these obstacles in developing a system for Face Recognition.. This research deals with both face extraction and recognition, Firstly, Eigenfaces are eigenvectors of covariance matrix, representing given image space. Any new face image can then be represented as a linear combination of these Eigenfaces which can be found by Principal Component Analysis (PCA) for face extraction,and by Recurrent (Time Cycling) Back Propagation artificial neural network for face recognition. The whole system was performed by training using 120 color images (40 human faces with 3 poses) and testing using 40 color images. The images were taken from Collection of Facial Images: Faces95 by Computer Vision Science Research Projects. The results indicated that the proposed method lends itself to good extraction and classification accuracy relative to existing techniques.
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