Integration of Face and Gait Recognition via Transfer Learning: A Multiscale Biometric Identification Approach

The ubiquity of biometric identification systems and their applications is evident in today's world. Among various biometric features, face and gait are readily obtainable and thus hold significant value. Advances in computational vision and deep learning have paved the way for the integration of these biometric features at multiple scales. This study introduces a system for biometric recognition that synergises face and gait recognition through the lens of transfer learning. Feature extraction was accomplished using Inception_v3 and DenseNet201 algorithms, while classification was performed employing machine learning algorithms such as K-Nearest Neighbours (KNN) and Support Vector Classification (SVC). A unique dataset was constructed for this research, consisting of face and gait information extracted from video clips. The findings underscore the efficacy of integrating face and gait recognition, primarily through feature and score fusion, resulting in enhanced recognition accuracy. Specifically, the Inception_v3 algorithm was found to excel in feature extraction, and SVC was superior for classification purposes. The system achieved an accuracy of 98% when feature-level fusion was performed, and 97% accuracy was observed with score fusion using Decision Trees. The results highlight the potential of transfer learning in advancing multiscale biometric recognition systems.