Research On Gait Recognition Based On Step Length And Frequency Domain Characteristics

Gait recognition is an attractive direction in biometric and computer vision in recent years. It aims to recognize individuals by the people's walking gait pattern. A gait recognition system consists of three primary parts: moving target detection, feature extraction and gait recognition. We research on the three parts, and our work can be concluded in the following several aspects:First, we compared kinds of motion detection methods, According to our actual situation ,we chose the background subtraction methods to get the motion area. Threshold segmentation and morphologic operation were used to make the image binarization. We also used canny algorithm for edge detection, These jobs laid a good foundation for the following feature extraction works.Second, on the feature extraction aspect, we used step length feature to divide period. Two-dimensional discrete fourier transform was used to translate the binary image to the frequency domain, then we calculated the frequency energy of the four key frames, and extracted the mean value of them as the features, together with the body step length made a 5 dimensional feathers vector.At last, two supervised pattern classification technique called nearest neighbor algorithm (NN) and k-nearest neighbor algorithm (KNN) were performed for recognition. Extensive experimental results on CASIA database demonstrate that the proposed algorithm has an encouraging recognition performance with relatively lower computational cost.