Performance Comparison of Improved HOG,Gabor and LBP

In order to achieve effective expression of the facial features in complex environment,an improved face recognition method of histograms of oriented gradients(HOG) is proposed.Firstly the face image grid is set as a sampling window,in which HOG features are extracted.Then all grid HOG feature vectors are composed to realize the whole facial features expression.Finally,recognition is achieved with a nearest neighbor classifier.We further compare it with two popular face local features expression methods,Gabor wavelet and local binary pattern(LBP).With the optimistic HOG parameters,experimental results show that HOG features with less dimensions have better performance than the LBP features in the FERET face collection under complex changes of light and time environments.Meanwhile HOG features have advantages over Gabor wavelet with less computational time of feature extraction and smaller number of feature vector dimensions.