Abstract: Gesture recognition is a hot topic in the field of pattern recognition. By means of depth information of Kinect sensor, this paper proposes a gesture segmentation method and a gesture recognition model based on HOG features of grayscale image. Besides, this paper researches HOG features, analyzes the characteristics of the eigenvectors, and explores the influence of different feature dimensions on training machine and processing efficiency. Finally, SVM method is utilized to realize the classification of gesture recognition, in which the SVM parameters are optimized through a large number of experiment data and the rate of identification is compared and analyzed. It is shown that the proposed method has less dimension, high recognition rate, quick running, and stable performance such that it can meet the requirements of real-time gesture recognition.