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

Richard Jiang, Somaya Al-Maadeed, Ahmed Bouridane, Danny Crookes, M. Emre Celebi

Research output: Contribution to journalArticlepeer-review

24 Citations (Scopus)
7 Downloads (Pure)


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.
Original languageEnglish
Pages (from-to)1807-1817
JournalIEEE Transactions on Information Forensics and Security
Issue number8
Early online date21 Apr 2016
Publication statusE-pub ahead of print - 21 Apr 2016


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