Gait Recognition for Laboratory Safety Management Based on Human Body Pose Model

Most of the important data and equipment storage places, such as laboratories, mainly rely on manual management and various biometric systems to ensure their security. Currently, the commonly used biometric systems include facial recognition, fingerprint recognition, voice recognition and gait recognition. Gait is a unique way of moving for each person. Compared with other biometrics, gait is difficult to imitate or fake and can accomplish the supervision tasks more efficiently. This paper proposes a novel Human Body Pose (HBP) model for gait recognition in laboratory environments. Specifically, we first extract the image of each frame from the video and extract the 2D human body poses in the form of people's joints and bones with OpenPose. Then we use a 3D pose library to estimate a 3D human pose by matching with the 2D pose. Finally, we employ a Convolutional Neural Network to extract the human temporal-spatial features for gait recognition. We train and validate our method to compare with the state-of-the-art methods on the CASIA gait dataset B. Experimental results show that our method outperforms the state-of-the-art methods in the case of cross-view and clothing changes.