Smart face identification via improved LBP and HOG features

Abstract

Smart face identification is widely used in smart city and smart healthcare. However, smart face identification technology is susceptible to envirnmental factor, such as illumination, mask, and expression. In order to fully extract facial feature information, we fuse an improved local binary pattern (LBP) and the histogram of oriented gradients (HOG) to extract the texture and detailed features on the face. The 2DPCA + PCA is used to reduce the dimensionality of the extracted features. The 2DPCA sloves the issue that the model is too complex when the feature dimension is very high. The feature reduction reduces the calculation scale and increases the calculation speed. Finally, experimental results on ORL and Yale face databases show that the feature extraction based on the fusion of improved LBP and HOG complement with each other. Compared with other recognition algorithms, the improved algorithm has higher recognition and identification rate.