A new Uyghur face recognition algorithm

Abstract

In this paper, in light of the reduced recognition rate and poor robustness of Uyghur face images under non-uniform illumination, a Histogram of Oriented Gradients (HOG) algorithm combining Discrete Cosine Transform (DCT) with Laplace filter was proposed. Firstly, the Uyghur face images were processed by Laplace filter to highlight their edges and texture features and reduce the interference of illumination; secondly, the Uyghur face images processed by Laplace filter were transformed by DCT, which can effectively filter the high frequency information and retain the low frequency information resulted from the DCT; thirdly, the Uyghur face images were reconstructed by Inverse Discrete Cosine Transform (IDCT), and the Uyghur face images reconstructed by IDCT were still similar to the original Uyghur face images, so that the non-low frequency information in the Uyghur face images was removed and the dimension of the Uyghur face images was reduced; finally, the inherent features of the final Uyghur face images were extracted using Histogram of Oriented Gradients (HOG) operator, and the final Uyghur face images were classified using the Nearest Neighbor Method. The simulation experiment results showed that the proposed algorithm further improved the recognition rate of the face images in face database — as high as 95% in the Yale B face database under different feature dimensions, and 98.5% in the Uyghur face database built by the members of the research group, respectively, which was superior to other traditional algorithms, and had strong robustness and good real time performance.