Hybrid Deep Learning Models for Gait Recognition: A Comparative Analysis of CNN, CNN-LSTM, and HOA Techniques

Gait recognition is a critical biometric technique with applications in surveillance, healthcare, and security. This study proposes a hybrid deep learning framework combining Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and the Hippopotamus Optimization Algorithm (HOA) for robust gait recognition. By leveraging spatial feature extraction, temporal dynamics, and metaheuristic hyperparameter optimization, the proposed HOA-CNN-LSTM model achieves superior performance. Experimental results on the TUM-GAID dataset show that the hybrid model outperforms standalone CNN and CNN-LSTM approaches in accuracy, processing time, and error rates. The findings suggest that HOA-optimized architectures provide scalable and efficient solutions for gait recognition tasks in real-world settings.