Lightweight Face

Lightweight face processing focuses on developing efficient algorithms and models for face-related tasks, such as detection and recognition, that minimize computational resources while maintaining high accuracy. Current research emphasizes lightweight convolutional neural networks (CNNs), often employing techniques like model pruning, quantization, and the use of efficient architectures such as MobileNet and ShuffleNet variants, to achieve this balance. This area is crucial for deploying face-related applications on resource-constrained devices like smartphones and embedded systems, impacting fields ranging from security and surveillance to human-computer interaction.