Efficient live face detection to counter spoof attack in face recognition systems

Among the various biometric tools, face recognition is used in almost all modern 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 a security system. In this research work, a robust technique is proposed which detects liveness of faces in order to counter spoof attacks using the three-dimensional (3D) fast Fourier transform which compares 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 using real life datasets show that the proposed technique yields excellent results for identifying live faces.