Abstract:

It is difficult to use the traditional algorithm to achieve the ideal result of face recognition under the influences of light, angle and other negative factors and this paper presents a robust face recognition algorithm based on Gaussian mixture model. First of all, ach of the sub image is divided into sub blocks, and the orientation gradient histogram feature is extracted, and corresponding spatial location information of sub block is used to generate local feature vectors of face image. Secondly local feature vectors of all the images are used to train Gaussian mixture model to generate feature vectors. Finally, least squares support vector machine is used to build classifiers for face recognition and match and recognize face. The simulation test was carried out with ORL, Yale, and CIGIT face database, the simulation results show that the face recognition rate of the proposed algorithm is far higher than those of the contrast face recognition algorithms, and it has stronger robustness for light, angle and expression, and can obtain the faster face recognition speed, and has higher practical values.

Key words: face recognition, extraction feature, Gaussian mixture model, least squares support vector machine