Abstract: In this paper, the K-L expansion for feature extraction has been combined with an incoherent optical correlator, which was previously constructed for human face recognition. In this new approach, the eigenfaces are used as the image filters in the reference plane of the correlator. Since the face images can be approximated by different linear combinations of a relatively few eigenfaces, they can be efficiently distinguished from one another by a small set of the weight coefficients, which is derived by projecting the input image onto every eigenface. The optical correlator is used as the feature extractor and the optical correlation results between the input image and the eigenfaces are used as the features. As a result, the recognition features can be got at a relatively high speed. Because the face images in the training set are selected to representing some typical distortions, the system can deal with the distortions to a large extent. !6
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