Human Gait Recognition based on Integrated Gait Features using Kinect Depth Cameras

Biometrics are widely used for security authentication systems to verify a person's identity such as fingerprint, iris, face, and voice recognition. Among them, unlike other biometrics, human gait has the advantage that it can be captured in an unobtrusive manner. In our previous research, we proposed a method of modeling the body parts of a captured walking person using the Kinect depth cameras. In this paper, we propose a new human gait recognition method that uses gait features extracted from the modeled body parts to identify a walking person. The proposed method uses a combination of static and dynamic gait features to improve the accuracy of person identification. Because each gait has a different cycle length, we also use a time normalization technique to transform gait feature sequences with different lengths to those of the same length to compare them more precisely. Based on the time-normalized gait feature sequences, we build a k-NN classifier and an LSTM classifier to classify different walking persons. Our experimental results show the high potentiality of the proposed method for identifying unknown walking persons.