BAHGRF3: Human gait recognition in the indoor environment using deep learning features fusion assisted framework and posterior probability moth flame optimisation

Abstract

Biometric characteristics are playing a vital role in security for the last few years. Human gait classification in video sequences is an important biometrics attribute and is used for security purposes. A new framework for human gait classification in video sequences using deep learning (DL) fusion assisted and posterior probability-based moth flames optimization (MFO) is proposed. In the first step, the video frames are resized and fine-tuned by two pre-trained lightweight DL models, EfficientNetB0 and MobileNetV2. Both models are selected based on the top-5 accuracy and less number of parameters. Later, both models are trained through deep transfer learning and extracted deep features fused using a voting scheme. In the last step, the authors develop a posterior probability-based MFO feature selection algorithm to select the best features. The selected features are classified using several supervised learning methods. The CASIA-B publicly available dataset has been employed for the experimental process. On this dataset, the authors selected six angles such as 0°, 18°, 90°, 108°, 162°, and 180° and obtained an average accuracy of 96.9%, 95.7%, 86.8%, 90.0%, 95.1%, and 99.7%. Results demonstrate comparable improvement in accuracy and significantly minimize the computational time with recent state-of-the-art techniques.