Landmark-Based Adversarial Network for RGB-D Pose Invariant Face Recognition

Even though numerous studies have been conducted, face recognition still suffers from poor performance in pose variance. Besides fine appearance details of the face from RGB images, we use depth images that present the 3D contour of the face to improve recognition performance in large poses. At first, we propose a dual-path RGB-D face recognition model which learns features from separate RGB and depth images and fuses the two features into one identity feature. We add associate loss to strengthen the complementary and improve performance. Second, we proposed a landmark-based adversarial network to help the face recognition model extract the pose-invariant identity feature. Our landmark-based adversarial network contains a feature generator, pose discriminator, and landmark module. After we use 2-stage optimization to optimize the pose discriminator and feature generator, we removed the pose factor in the feature extracted by the generator. We conduct experiments on KinectFaceDB, RealSensetest and LiDARtest. On KinectFaceDB, we achieve a recognition accuracy of 99.41%, which is 1.31% higher than other methods. On RealSensetest, we achieve a classification accuracy of 92.57%, which is 30.51% higher than other methods. On LiDARtest, we achieve 98.21%, which is 21.88% higher than other methods.