SSPP-DAN: Deep Domain Adaptation Network for Face Recognition with Single Sample Per Person

TL;DR

This paper introduces SSPP-DAN, a deep domain adaptation network that enhances face recognition with a single sample per person by combining domain adaptation and synthetic image generation, achieving state-of-the-art results.

Contribution

The paper presents a novel deep domain adaptation network that uses synthetic pose variations to improve SSPP face recognition performance.

Findings

Significant accuracy improvement with synthetic images and domain adaptation.

Achieved state-of-the-art results on benchmark datasets.

Demonstrated the effectiveness of joint training with synthetic data.

Abstract

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).