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

Jiang, Richard, Al-Maadeed, Somaya, Bouridane, Ahmed, 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

07456303.pdf - Published Version
Available under License Creative Commons Attribution.

Download (2MB) | Preview
[img] Text (Full text)
Face_TIFS_Final.pdf - Accepted Version
Restricted to Repository staff only

Download (2MB)
Official URL: http://dx.doi.org/10.1109/TIFS.2016.2555792


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 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 chaotic signals. We also incorporate a salience-aware strategy into the proposed ensemble method to handle chaotic facial patterns in the scrambled domain, where random selections of features are made on semantic components via salience modelling. In our experiments, the proposed MK-RDA was tested rigorously on three human face datasets: the ORL face dataset, the PIE face dataset and the PUBFIG wild face dataset. The experimental results successfully demonstrate that the proposed scheme can effectively handle chaotic signals and significantly improve the recognition accuracy, making our method a promising candidate for secure biometric verification in emerging IoT applications.

Item Type: Article
Uncontrolled Keywords: Facial biometrics, face scrambling, many manifolds, many kernels, random discriminant analysis, mobile biometrics, Internet-of-Things, user privacy
Subjects: G400 Computer Science
G500 Information Systems
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Paul Burns
Date Deposited: 04 May 2016 11:10
Last Modified: 01 Aug 2021 08:32
URI: http://nrl.northumbria.ac.uk/id/eprint/26747

Actions (login required)

View Item View Item


Downloads per month over past year

View more statistics