Deep Neural Network Gait Recognition in Habitoscopy Learning Process

This article proposes a new method for human gaits classification. Deep neural networks were used to classify gait features. The public database CASIA Gait Dataset B were used for testing. The proposed method achieved the best rank-l identification rate of 83.78%, and rank-5–98.65%. This method can used for studying habitoscopy by students.