AN ADAPTIVE FACE RECOGNITION IN COMBINED GLOBAL AND LOCAL PRESERVING FEATURE SPACE
Computer face recognition promises to be a powerful tool and is becoming important in our security-heightened world. 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 finding the minimum distance or maximum variance among the training and testing feature set. This leads to the wrong classification when presenting the untrained image or unknown image, since the classification process locates at least one winning cluster having minimum distance or maximum variance among the existing clusters. But for the security related applications, these new facial image should be reported and necessary action has to be taken accordingly. In this paper, we propose the following two techniques for this purpose: (i) Use a threshold value calculated by finding the average of the minimum matching distances of the wrong classifications encountered during the training phase. (ii) Use the fact that the wrong classification increases the ratio of within-class distance and between-class distance. Experiments have been conducted using the ORL facial database and a fair comparison is made with the conventional feature spaces to show the efficiency of these techniques.
face recognition biometric technologies adaptive classification global features local features