LW-DCGAN: a lightweight deep convolutional generative adversarial network for enhancing occluded face recognition

Traditional facial recognition techniques often struggle to balance accuracy with model complexity. High accuracy typically demands intricate models, slowing recognition speeds on devices such as smartphones. Conversely, faster methods often sacrifice accuracy. We introduce a lightweight deep convolutional generative adversarial network (LW-DCGAN), designed specifically to address the challenges of occluded face recognition. By simplifying the network architecture and employing efficient feature extraction techniques such as transpose convolution, batch normalization, feature pyramid networks, and attention modules, we enhance both hierarchical sampling and contextual relevance. Furthermore, L1 regularization and channel sparsity techniques compress the model for resource-constrained environments. We thoroughly evaluate LW-DCGAN’s generalization and robustness, comparing its performance against other generative adversarial network variants and common face recognition models. The results demonstrate that LW-DCGAN achieves higher accuracy while significantly reducing model size and computational overhead, offering a promising advancement in lightweight face recognition technology.