Abstract
Face recognition using a single sample per person is a challenging problem in computer vision. In this scenario, due to the lack of training samples, it is difficult to distinguish between inter-class variations caused by identity and intra-class variations caused by external factors such as illumination, pose, etc. To address this problem, we propose a scheme to improve the recognition rate by both generating additional samples to enrich the intra-variation and eliminating external factors to extract invariant features. Firstly, a 3D face modeling module is proposed to recover the intrinsic properties of the input image, i.e., 3D face shape and albedo. To obtain the complete albedo, we come up with an end-to-end network to estimate the full albedo UV map from incomplete textures. The obtained albedo UV map not only eliminates the influence of the illumination, pose, and expression, but also retains the identity information. With the help of the recovered intrinsic properties, we then generate images under various illuminations, expressions, and poses. Finally, the albedo and the generated images are used to assist single sample per person face recognition. The experimental results on Face Recognition Technology (FERET), Labeled Faces in the Wild (LFW), Celebrities in Frontal-Profile (CFP) and other face databases demonstrate the effectiveness of the proposed method.
Highlights
Face recognition has been an active topic and attracted extensive attention due to its wide potential applications in many areas [1,2,3]
The method consists of three modules: 3D face modeling, 2D image generation, and improved SSPP FR
We evaluated the effectiveness of the proposed method from two aspects: (i) visual inspection of the reconstructed albedo and shape, and the generated facial images accuracy as well; and (ii) single sample per person face recognition based on enriching intra-variation and invariant features
Summary
Face recognition has been an active topic and attracted extensive attention due to its wide potential applications in many areas [1,2,3]. Compared with near infrared and depth images [4], RGB images include more information and have broader application scenarios. Face recognition with single sample per person, i.e., SSPP FR, proposed in 1995 by Beymer and Poggio [9], is one of the most important issues. In SSPP FR, there is only one training sample per person but various testing samples with appearance different from training samples. This situation could appear in many actual scenarios such as criminal tracing, ID card identification, video surveillance, etc. In SSPP FR, the limited training samples provide insufficient information of intra-class variations, which significantly decreases the performance of most existing face recognition methods.
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