Real-time face recognition algorithm based on lightweight neural network in the field of computer vision

Traditional face recognition systems deployed on mobile and embedded devices often suffer from limited computing power and high energy consumption. To address these challenges, this study proposes a real-time face recognition algorithm based on a lightweight neural network, called the Light Face Network, aiming to improve detection speed, recognition accuracy, and energy efficiency under constrained hardware environments. The model integrates depthwise separable convolutional layers, channel-attention mechanisms, and a feature-compression strategy to reduce computational complexity while maintaining precision. Experimental results show that the proposed network achieves a recognition accuracy of 97.1% for anger emotion and maintains an overall accuracy above 90% on public benchmark datasets including Labeled Faces in the Wild and Age Database. Under partial occlusion scenarios such as sunglasses and scarf coverings, the recognition accuracy reaches 98.8% on smartphone platforms and 95.7% on surveillance cameras, while maintaining low energy consumption levels of approximately 3550 mW and 3800 mW, respectively. These findings demonstrate that the lightweight neural-network-based model can achieve high-precision and energy-efficient face recognition in real-time applications, providing a robust foundation for intelligent monitoring and human-computer interaction systems.