A face recognition scheme based on spectral domain cross-correlation function

This paper presents a simple yet efficient face recognition technique, where a feature extraction algorithm is proposed based on the principle of spectral domain cross-correlation. Instead of considering the spatial variation of a face image as a whole, first we concentrate on spectral variation of each row of the image individually, which is obtained using discrete cosine transform (DCT). As each of these rows could carry distinct characteristic of the face image, considering all of them would ensure the extraction of the variation in face geometry without discarding any information even in a minute scale. It is shown that the cross-correlations in the DCT-domain considering pairs of consecutive rows provide a signature of the particular face image reflecting the variation in the face geometry along the vertical direction. In a similar fashion, a horizontal signature can be obtained considering DCT-domain cross-correlations along consecutive columns. However, it is observed that in comparison to horizontal features, vertical features offer better within-class compactness and between-class separation. In the proposed method, these two signatures are utilized combinedly in order to obtain a distinguishable feature space. From extensive simulations on standard databases, it is found that the proposed feature extraction algorithm offers advantages of simple practical implementation with a high degree of face recognition accuracy.