An efficient method for extracting features in facial images for human face recognition system

This paper introduces an efficient method for the recognition of human faces in 2-Dimensional digital images using a new feature extraction method. The proposed feature extraction method includes human face localization derived from the shape information using a new distance measure as Facial Candidate Threshold (FCT) as well as Pseudo Zernike Moment Invariant (PZMI). Also we introduce a new parameter to define Axis Correction Ratio (ACR) of images for disregarding irrelevant information of face images. In this paper the effect of the new parameters in disregarding irrelevant information in recognition rate is studied. Also we evaluate the effect of orders of PZMI in the proposed technique. Simulation results on the face database of the Yale indicate that high order PZMI together with derived face localization and proposed technique for feature extraction contain very useful information about face recognition process. Recognition rate of 99.7% is obtained using this proposed technique.