Application of GAN-based facial reconstruction and recognition system based on dense connection modules

To address the issues of detail distortion and difficulty in preserving identity features in existing facial restoration methods under complex damage conditions, a collaborative restoration and recognition framework based on densely connected generative adversarial networks is proposed. This method integrates multi-scale convolution, densely connected residual blocks, and an attention mechanism, and introduces an identity consistency constraint to achieve joint optimization of restoration and recognition. Different from existing methods, this framework explicitly embeds identity-preserving constraints into the restoration process through a dual-path collaborative optimization of restoration and recognition, thereby jointly improving visual quality and recognition performance. Experimental results show that, in terms of restoration error, under 70% occlusion, the perceptual loss is 0.42, the recall rate reaches 70.2%, the structural consistency error is 0.06, the discriminator realism confidence score is 0.94, and the recognition accuracy remains at 71.3% in noisy environments. This method effectively improves the realism and identity preservation ability of restored images and is suitable for highly robust restoration and recognition tasks.