Introduction - If you have any usage issues, please Google them yourself
In order to achieve facial features in complex environments valid expression, an improved gradient direction histogram (HOG) face recognition method. Firstly face image and extract the grid as a sample window HOG features on it then all mesh HOG feature vector combination, realize the whole people express facial feature Finally, nearest neighbor classifier to identify. In addition, the comparison of the method with Gabor wavelet and local binary pattern (LBP) 2 famous facial features indicate the quality of the local approach. Experimental results show that HOG parameter tuning in FERET face with complex changes in the environment of light and time, the characteristic dimension of less than HOG LBP features better performance, and feature extraction time and HOG dimension of feature vectors have an advantage over Gabor wavelet method