Name
Abstract | ||
|---|---|---|
•A novel end-to-end deep rank hashing (DRH) network is proposed for optimal identification and rank hashing goals. The network is trained independently from the enrollee face image to avoid security leakage and retraining for newly enrolled users.•A pairwise margin-based angular loss, code balancing loss, and quantization error corrector designed for nonlinear subspace ranking hashing are proposed. Based on the combination of proposed loss functions, the DRH learns to generate compact, high discriminative, and consistent binary hash codes under open-set settings.•A novel cancellable identification scheme is proposed based on the one-time XOR cipher notion.•Comprehensive experiments and security analyses are carried out based on the five face datasets verification, open-set, and closed-set identification protocols. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.patcog.2022.108886 | Pattern Recognition |
Keywords | DocType | Volume |
Cancellable biometrics,Deep learning,Face biometrics,Hashing,Identification | Journal | 131 |
Issue | ISSN | Citations |
1 | 0031-3203 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dong Xingbo | 1 | 0 | 1.69 |
| Sangrae Cho | 2 | 38 | 6.72 |
| Youngsam Kim | 3 | 0 | 0.34 |
| Soo-Hyung Kim | 4 | 191 | 49.03 |
| Andrew Beng Jin Teoh | 5 | 1778 | 125.80 |