Abstract
Single-sample face recognition is a very challenging problem, where each person has only one labeled training sample. It is difficult to describe unknown facial variations. In this paper, we propose a shared generative adversarial network (SharedGAN) to expand the gallery dataset. Benefiting from the shared decoding network, SharedGAN requires only a small number of training samples. After obtaining the generated samples, we join them into a large public dataset. Then, a deep convolutional neural network is trained on the new dataset. We use the well-trained model for feature extraction. With the deep convolutional features, a simple softmax classifier is trained. Our method has been evaluated on AR, CMU-PIE, and FERET datasets. Experimental results demonstrate the effectiveness of SharedGAN and show its robustness for single sample face recognition.
Highlights
Considering the above three points, in this paper, we propose a novel scheme to fulfill the task of single-sample face recognition
We propose a shared generative adversarial network to generate virtual samples for the gallery dataset
By improving the decoding ability of the shared network, SharedGAN indirectly improves the robustness of the image translation model
Summary
Face recognition has been one of the hottest topics in the field of computer vision and pattern recognition. Many face recognition technologies have been proposed in view of various situations, among which single-sample face recognition is a very challenging problem. In many practical applications, such as access control, passport identification, judicial confirmation, etc., only a single sample per person (SSPP) is enrolled in the gallery dataset for training. When the probe sample is affected by factors such as illumination, expression, and occlusion, the single-sample face recognition task becomes more difficult. Traditional face recognition methods [1–3] usually assume that each person has multiple training samples. These methods will face serious performance degradation when dealing with the single-sample face recognition task
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