Name
Abstract | ||
|---|---|---|
This paper studies face recognition (FR) and normalization in surveillance imagery. Surveillance FR is a challenging problem that has great values in law enforcement. Despite recent progress in conventional FR, less effort has been devoted to surveillance FR. To bridge this gap, we propose a Feature Adaptation Network (FAN) to jointly perform surveillance FR and normalization. Our face normalization mainly acts on the aspect of image resolution, closely related to face super-resolution. However, previous face super-resolution methods require paired training data with pixel-to-pixel correspondence, which is typically unavailable between real low- and high-resolution faces. Our FAN can leverage both paired and unpaired data as we disentangle the features into identity and non-identity components and adapt the distribution of the identity features, which breaks the limit of current face super-resolution methods. We further propose a random scale augmentation scheme to learn resolution robust identity features, with advantages over previous fixed scale augmentation. Extensive experiments on LFW, WIDER FACE, QUML-SurvFace and SCface datasets have demonstrated the superiority of our proposed method compared to the state of the arts on surveillance face recognition and normalization. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/978-3-030-69532-3_19 | ACCV (2) |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yin Xi | 1 | 0 | 0.34 |
| Ying Tai | 2 | 213 | 25.74 |
| Huang Yuge | 3 | 0 | 0.68 |
| Xiaoming Liu | 4 | 1627 | 93.31 |