SVM-based one-against-many algorithm for liveness face authentication

Illegal users are not permitted to operate within a secure environment. To establish the legality of authentication, the authentication system must perceive and refuse a fake biometric over liveness face authentication. In order to achieve reliable liveness face authentication, the intended purpose of the proposed framework should have two major parts: liveness detection and face authentication. The proposed liveness detection describes illuminative variations on the face, which is especially applicable in artificial shadow estimation; face authentication should also employ a one-against-many classification algorithm based on Support Vector Machine (SVM) to obtain individual subsets, then estimates authenticated performances. Based on experiments on liveness XM2VTS database and photographs from the Google Picasa database, we achieved the liveness accuracy rate of 96.5%, the false rejection rate of 1.17% and the false acceptance rate of 1.69%.