Face Recognition in the Scrambled Domain via Salience-Aware Ensembles of Many Kernels

Jiang, Richard and Al-Maadeed, Somaya and Bouridane, Ahmed and Crookes, Danny and Celebi, M. Emre (2016) Face Recognition in the Scrambled Domain via Salience-Aware Ensembles of Many Kernels. IEEE Transactions on Information Forensics and Security, 11 (8). pp. 1807-1817. ISSN 1556-6013

Full text not available from this repository.


With the rapid development of Internet-of-Things (IoT), face scrambling has been proposed for privacy protection during IoT-targeted image/video distribution. Consequently, in these IoT applications, biometric verification needs to be carried out in the scrambled domain, presenting significant challenges in face recognition. Since face models become chaotic signals after scrambling/encryption, a typical solution is to utilize the traditional data-driven face recognition algorithms. While chaotic pattern recognition is still a challenging task, in this paper, we propose a new ensemble approach-many-kernel random discriminant analysis (MK-RDA)-to discover discriminative patterns from the chaotic signals. We also incorporate a salience-aware strategy into the proposed ensemble method to handle the chaotic facial patterns in the scrambled domain, where the random selections of features are made on semantic components via salience modeling. In our experiments, the proposed MK-RDA was tested rigorously on three human face data sets: the ORL face data set, the PIE face data set, and the PUBFIG wild face data set. The experimental results successfully demonstrate that the proposed scheme can effectively handle the chaotic signals and significantly improve the recognition accuracy, making our method a promising candidate for secure biometric verification in the emerging IoT applications.

Item Type:
Journal Article
Journal or Publication Title:
IEEE Transactions on Information Forensics and Security
Uncontrolled Keywords:
ID Code:
Deposited By:
Deposited On:
19 Mar 2019 09:15
Last Modified:
22 Nov 2022 07:12