Multimedia based fast face recognition algorithm of speed up robust features

For the problem that the HAAR descriptors in the speed up robust feature (SURF) algorithm cannot make full use of the information around the feature points, the K-Mean clustering technology is used in this paper to improve the SURF, thus proposing a new face recognition algorithm. Firstly, the problem that the main direction is too dependent on the direction of the local area is avoided by expanding the scope of the main direction; then the information around the subblock that is masked by the 3 x 3 window template is made full use of to construct a descriptor with stronger recognition ability; finally, the problems of excessive time consumed and incorrect matching of interest points are solved by introducing the K-Mean clustering idea. The results of the experiment on FERET and Yale face database show that the proposed algorithm has higher recognition rate and efficiency than other face recognition techniques.