The Excellent Properties of a Dense Grid-Based HOG Feature on Face Recognition Compared to Gabor and LBP

Abstract

To effectively represent facial features in complex environments, a face recognition method based on dense grid histograms of oriented gradients (HOG) is proposed. First, the face image is divided by numerous dense grids from which the HOG features are extracted. Then, all the grid HOG feature vectors are composed to realize the feature expression of the whole face, and the nearest neighbor classifier is used for recognition. In the FERET face database with complex changes in illumination, time, and environment, we test the gamma illumination correction, the spatial gradient direction, the size of the block, the standardization, and the face image resolution to find and analyze the optimal HOG parameters for face recognition. Finally, we compare our dense grid HOG with the two famous local facial feature extraction methods: the Gabor wavelet and the local binary pattern (LBP) on face recognition. The experimental results show that the dense grid HOG method is more suitable for the variations in time and environment. The feature extraction times of the dense grid HOG and LBP are similar. However, the dense grid HOG method uses fewer dimensions to obtain a better recognition rate than the LBP. Moreover, the dense grid HOG feature extraction time greatly outperforms the Gabor wavelet feature.