DiEL: Disentangled Evolutionary Learning for Identity-Preserving Face Enhancement and Recognition

DiEL is a method for improving face images while keeping the person's identity recognizable, especially in difficult conditions like extreme angles, blurriness, or poor lighting. The approach uses evolutionary learning (a technique that evolves solutions over time) to separate identity information from pose information (head angle), then reconstructs faces using a pose dictionary (a library of standard face angles learned from many images) to maintain consistency. The method outperforms existing approaches by an average of 4.66% on six benchmark datasets, with particularly strong improvements on challenging cross-pose tests.