N-mean kernel filter and normalized correlation for face localization

Recently, Template matching approach has been widely used to locate faces with various pose, illumination and clutter background. Normalized Cross-correlation (NCC) is an effective and simple measurement method to compute the similarity matching between the stored faces templates and the rectangular blocks of the input image to locate the face position. However, localization error occurs very often due to some rectangular blocks which have more face than correct blocks because of the effect of matrices values of these blocks. In this paper we proposed a simple preprocessing method before the use of NCC. This is to reduce the effects of such problems by increasing the values of the input image pixels. The result showed a significant improvement in localization accuracy compared with the use of NCC alone which is only up to 11%. Yale University database was used to evaluate our proposed method.