Identification of Untrained Facial Image in Combined Global and Local Preserving Feature Space

In real time applications, biometric authentication has been widely regarded as the most foolproof - or at least the hardest to forge or spoof. Several research works on face recognition based on appearance, features like intensity, color, textures or shape have been done over the last decade. In those works, mostly the classification is achieved by using the similarity measurement techniques that find the minimum distance among the training and testing feature set. When presenting this leads to the wrong classification when presenting the untrained image or unknown image, since the classification process locates at least one wining cluster that is having minimum distance or maximum variance among the existing clusters.