Adaptive hybrid technique for face recognition

One of the most important biometric features for personal identification is the face. In current paper, a new method of face verification upon on singular value decomposition (SVD) and standard deviation (SD) would be described. Due to many variations in real-life such as pose, illumination, or facial expression, there would be difficulty of face recognition. It should be mentioned that there are many approaches for face recognition, however, there is no one could be considered as the most suitable for many situations. One of the methods used is Singular value vector for an image detecting, but the drawback of this approach is the low rate of recognition, where one scale singular value vector is used for face acknowledgment. There an algorithm has been developed to expand the rate of the recognition. In this paper, an approach has been proposed to associate two feature sets obtained from SVD and SD method. It has noticed a good recognition rate could be obtained from the experimental results, where approximately more that 97.5% recognition rate has obtained on the ORL data base. The results from current proposed method have matched with some techniques and it has shown that this method is better than the existing approaches. An extensive experiment has demonstrated not only better performance, but it offers a great likely to achieve equivalent performance to other categories of state-of-the-art method.