DCNN-RRDA: A Hybrid Deep Learning and Evolutionary Optimization Framework for Robust Face Recognition

Abstract

Face recognition has emerged as a critical biometric technology for applications in surveillance, authentication, and security. However, variations in illumination, pose, and facial expressions significantly reduce recognition accuracy in practical environments. This research proposes a robust and adaptive framework, DCNN-RRDA, which integrates traditional feature extraction, deep learning, and evolutionary optimization. Facial features are initially captured using Histogram of Oriented Gradients (HOG), followed by hierarchical feature learning through a Deep Convolutional Neural Network (DCNN). Hyperparameters and network weights are optimized using the Refined Red Deer Algorithm (RRDA), which incorporates adaptive, chaotic, and inverse learning strategies to enhance convergence and avoid local optima. The framework was trained and evaluated on a publicly available Kaggle face dataset comprising approximately 16,700 images. Performance was assessed using accuracy, precision, recall, and F1-score, and compared against baseline models including Inception v3, ResNet50, and VGG16. Experimental results show that DCNN-RRDA achieved 98.54% accuracy, 97.46% precision, 96.97% recall, and 95.88% F1-score, consistently outperforming baseline models under challenging conditions such as noise, occlusion, and illumination changes. The proposed hybrid approach demonstrates that combining deep hierarchical learning with evolutionary optimization can significantly improve recognition reliability, generalization, and robustness. These results suggest its practical applicability in real-world intelligent security systems, while highlighting the potential for further enhancements in real-time deployment and lightweight implementations for resource-constrained environments.