Gait Recognition Using Zernike Moments and BP Neural Network

A new gait recognition method based on Zernike moments and BP neural network is proposed. Zernike moments are calculated to extract gait features based on the introduced concept of normalized gait cycle. All gait Zernike moments compose the gait feature space. PCA algorithm is used to compress Zernike moments and a new lower dimension feature space containing gait spatio-temporal features is generated. Each normalized gait cycle´s Zernike moments are mapped to this new feature space and compose an eigen-matrix, whose row square error vectors are used as the gait recognition eigenvectors. BP neural network is used to classify the gait features. To increase recognition accuracy, multiple training samples and multiple inputs are used for each to be recognized gait class. Experimental results show that the method can obtain accurate gait recognition in relatively simple scenes.