Gait identification by convolutional neural networks and optical flow

Abstract

Non-interactive biometric systems have gained an enormous interest from computer vision researchers as they provide more efficient and reliable ways of identification and authorization from a distance. Gait and face recognition are types of non-interactive biometric systems without users’ cooperation with the surveillance system. On the contrary to face recognition, gait recognition can manage low-resolution and low-brightness images. It aims to know the individuals based on their style and way of walking. Gait recognition has numerous applications in several domains, such as healthcare monitoring, security systems, and surveillance systems for indoor and outdoor activities. Yet, gait recognition performance is frequently deteriorated by some variety of factors, such as viewing angle variations, and clothing changes. Recently, deep learning models have been employed efficiently in gait recognition systems. They are more generic, since the feature construction process is completely automated. This paper presents gait features measured automatically in the midst of walking for the recognition system. To extract these features from a video of a moving object, two vital modules are used, namely the motion detection and tracking, and the feature extraction. Accordingly, the principal module serves to distinguish the walking style in an image sequence or video. A background subtraction technique is executed to fragment the movement of the background, and the moving area related to the spatial silhouette is correctly tracked and segmented. The second module “Feature Extraction” is used to extract the features from the sequence of silhouette images. The gait cycle is calculated from the shape changes of the silhouettes, and it is used to construct a small sequence of Gait Energy Images (GEI). The optical flow of the GEI is measured to extract only the moving parts and exclude the static ones. Finally, the Convolution Neural Network (CNN) is fed with the optical flow output to build unique features. These features are used for neural network training, and evaluation is performed on popular gait benchmark datasets. The obtained results reveal an accuracy level of 95% with more resistance to view and probe changes.