A face recognition scheme based on spatial correlation function

This paper presents a simple yet efficient face recognition technique, where a feature extraction algorithm is proposed based on the principle of spatial cross-correlation. Instead of considering the spatial data of a face image as a whole, we concentrate on each row and column of the data individually. As each of these rows and columns 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 obtained 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 the change in cross-correlations along the horizontal directions. In the proposed method, both of these horizontal and vertical signatures are utilized in order to obtain a distinguishable feature space for the face image of a particular person. It is found that the proposed feature extraction algorithm can provide a satisfactory face recognition performance for different standard databases even utilizing a simple distance based classifier. Thus, the advantage of this proposed method is that it offers a very simple real life implementation without sacrificing the recognition accuracy.