Advancements in Gait Recognition: A Study on Gait Energy Images and Gait Entropy Images

Gait recognition is a promising biometric modality due to its non-invasive nature and difficulty to disguise. However, the performance still lacks compared to other, well established biometric modalities. This paper presents results of our study on gait recognition, focusing on the comparison between Gait Energy Images (GEI) and Gait Entropy Images (GEnI) under various conditions. Different methodologies are explored, including deep learning techniques and Vision Transformers (ViTs), for feature extraction and classification. The popular CASIA-B dataset is used to evaluate the performance across different walking conditions and entropy measures. The effectiveness of gait recognition systems in accurately identifying individuals is shown, thus highlighting the potential of GEnI in enhancing the recognition performance under varying conditions.