Deep rank hashing network for cancellable face identification
作者：
Highlights：
• 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.
摘要
•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.
论文关键词：Cancellable biometrics,Deep learning,Face biometrics,Hashing,Identification
论文评审过程：Received 4 January 2021, Revised 13 May 2022, Accepted 2 July 2022, Available online 4 July 2022, Version of Record 14 July 2022.
论文官网地址：https://doi.org/10.1016/j.patcog.2022.108886
