Low-quality facial biometric verification via dictionary-based random pooling

Al-Maadeed, Somaya, Bourif, Mehdi, Bouridane, Ahmed and Jiang, Richard (2016) Low-quality facial biometric verification via dictionary-based random pooling. Pattern Recognition, 52. pp. 238-248. ISSN 0031-3203

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Official URL: https://doi.org/10.1016/j.patcog.2015.09.031


In the past decade, visual surveillance has emerged as an effective tool in public security applications. Due to the technical limitations of both surveillance cameras and transmission speed, videos collected from surveillance sites are usually of low resolution. Especially, facial images at a distance in surveillance videos are usually at very low quality, making it difficult to carry out automated facial biometric verification. To handle with this challenge, in this work, we introduce dictionary based techniques to cope with low quality facial images, and propose a random pooling scheme to enhance the accuracy of facial biometric verification. In the proposed scheme, a dictionary is first learned from paired low-resolution and high-resolution facial images, and the input low-resolution query face can then be modelled by a set of high-resolution visual words via a dictionary lookup. A random pooling strategy is then applied to select subsets of visual words, and kernel Fisher׳s linear discriminant analysis (k-LDA) is introduced to find the discriminant metrics. The final decision is based on the average over different pooling results. The experiment on three publically available face datasets validated that our proposed scheme can robustly cope with the challenges from low quality facial images, and attained an improved accuracy over all datasets, making our method a promising candidate for facial biometric based security applications.

Item Type: Article
Uncontrolled Keywords: facial biometrics, low resolution, sparse coding, random pooling, Kernel LDA, visual surveillance
Subjects: G400 Computer Science
G700 Artificial Intelligence
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Ay Okpokam
Date Deposited: 30 Nov 2015 11:22
Last Modified: 10 Oct 2019 18:49
URI: http://nrl.northumbria.ac.uk/id/eprint/24806

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