An improved shifted phase-encoded fringe-adjusted joint transform correlation technique is proposed in this paper for face recognition which can accommodate the detrimental effects of noise, illumination, and other 3D distortions such as expression and rotation variations. This technique utilizes a third order local derivative pattern operator (LDP3) followed by a shifted phase-encoded fringe-adjusted joint transform correlation (SPFJTC) operation. The local derivative pattern operator ensures better facial feature extraction in a variable environment while the SPFJTC yields robust correlation output for the desired signals. The performance of the proposed method is determined by using the Yale Face Database, Yale Face Database B, and Georgia Institute of Technology Face Database. This technique has been found to yield better face recognition rate compared to alternate JTC based techniques.
Face recognition is a critical tool used in almost all major biometrics based security systems. But recognition,
authentication and liveness detection of the face of an actual user is a major challenge because an imposter or a non-live
face of the actual user can be used to spoof the security system. In this research, a robust technique is proposed which
detects liveness of faces in order to counter spoof attacks. The proposed technique uses a three-dimensional (3D) fast
Fourier transform to compare spectral energies of a live face and a fake face in a mathematically selective manner. The
mathematical model involves evaluation of energies of selective high frequency bands of average power spectra of both
live and non-live faces. It also carries out proper recognition and authentication of the face of the actual user using the
fringe-adjusted joint transform correlation technique, which has been found to yield the highest correlation output for a
match. Experimental tests show that the proposed technique yields excellent results for identifying live faces.