SSPP-DAN: Deep Domain Adaptation Network for Face Recognition with Single Sample Per Person
This addresses the challenging problem of single-sample face recognition in real-world scenarios for applications like security and surveillance, though it is incremental by building on existing domain adaptation and synthesis techniques.
The paper tackles face recognition with only one sample per person under different capture conditions by introducing SSPP-DAN, a deep domain adaptation network that combines domain-adversarial training with synthetic image generation, achieving state-of-the-art performance on a benchmark dataset.
Real-world face recognition using a single sample per person (SSPP) is a challenging task. The problem is exacerbated if the conditions under which the gallery image and the probe set are captured are completely different. To address these issues from the perspective of domain adaptation, we introduce an SSPP domain adaptation network (SSPP-DAN). In the proposed approach, domain adaptation, feature extraction, and classification are performed jointly using a deep architecture with domain-adversarial training. However, the SSPP characteristic of one training sample per class is insufficient to train the deep architecture. To overcome this shortage, we generate synthetic images with varying poses using a 3D face model. Experimental evaluations using a realistic SSPP dataset show that deep domain adaptation and image synthesis complement each other and dramatically improve accuracy. Experiments on a benchmark dataset using the proposed approach show state-of-the-art performance. All the dataset and the source code can be found in our online repository (https://github.com/csehong/SSPP-DAN).