GAIT RECOGNITION USING DISENTANGLED REPRESENTATION LEARNING BASED ON INFORMATION ENTROPY

Gait recognition has a wide range of applications in real life. The key of gait recognition is to extract gait related features from the video frames of walking people. Aimed at the problem that the existing methods can not obtain gait features based on appearance features, using the disentangled representation learning method, an autoencoder architecture was proposed to decompose gait features and appearance features, and the joint entropy based on Renyi entropy was used to minimize the mutual information between gait features and appearance features. Through a large number of experiments on CASIA-B and FVC data sets, this method shows better decoupling ability and higher recognition accuracy in gait recognition.