Gait feature extraction and gait classification using two-branch CNN

As a promising biometric identification method, gait recognition has many advantages, such as suitable for human identification at a long distance, requiring no contact and hard to imitate. However, due to the complex external factors in the gait data sampling process and the clothing changes of the person to be identified, gait recognition still faces numerous challenges in practical applications. In this paper, we present a novel solution for gait feature extraction and gait classification. Firstly, two kinds of Two-branch Convolution Neural Network (TCNN), i.e., middle-fusion TCNN and last-fusion TCNN, to improve the correct recognition rate of gait recognition are presented. Secondly, we construct Multi-Frequency Gait Energy Images (MF-GEIs) to train the proposed TCNNs networks and then extract refined gait features using the trained TCNNs. Finally, the output of each TCNN is utilized to train an SVM gait classifier separately which will be used for gait classification and recognition. In addition, the proposed solution is measured on CASIA dataset B and OU-ISIR LP dataset. Both experimental results show that our solution outperforms various existing methods.