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

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