Face recognition based on principal component analysis (PCA) that uses eigenfaces is popular in face recognition markets. We present a comparison between various optoelectronic face recognition techniques and a PCA-based technique for face recognition. Computer simulations are used to study the effectiveness of the PCA-based technique, especially for facial images with a high level of distortion. Results are then compared with various distortion-invariant optoelectronic face recognition algorithms such as synthetic discriminant functions (SDF), projection-slice SDF, optical-correlator-based neural networks, and pose-estimation-based correlation.
© 2005 Optical Society of America
A. Alsamman and Mohammad S. Alam, "Comparative study of face recognition techniques that use joint transform correlation and principal component analysis," Appl. Opt. 44, 688-692 (2005)