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

Abstract

The purpose of face enhancement tasks is to improve the recognition of faces, thus adapting to diverse visualization and recognition demands. However, the performance of the majority methods is drastically degraded under extreme conditions, including large pose variations, low resolution, blur, occlusion, and illumination changes, which can distort facial geometry and identity related details. In this work, we construct a simple and effective face robust enhancement method. In particular, in order to maintain the identity consistency of the reconstructed face, an evolutionary learning framework for face disentanglement representation is proposed, in which we disentangle the identity and pose information of the face and unite it with identity recognition as a multi-objective optimization problem, where reconstruction, adversarial, and identity-preserving objectives are adaptively balanced. Further, in order to maintain the pose consistency of reconstructed faces, we construct a unified face pose dictionary, which forms a robust and standard pose representation by statistics and induction of the geometric structure of a large number of face images. In the conditional generation architecture, the pose dictionary could accurately guide the model to realize face reconstruction with desired poses. Extensive benchmark experiments on MS1M, LFW, CPLFW, CFP-FF, CFP-FP, and AgeDB show that the proposed method not only significantly outperforms state-of-the-art methods, but also can further stimulate the discrimination potential of existing face recognition models. Specifically, DiEL achieves an average improvement of 4.66% over the SOTA methods across six benchmark datasets, with particularly significant gains on challenging cross-pose benchmarks such as CPLFW and CFP-FP.