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Skin texture features for face recognition

Al-Qarni, Garsah Farhan (2013) Skin texture features for face recognition. Doctor of Philosophy (PhD) thesis, University of Kent. (doi:10.22024/UniKent/01.02.94168) (KAR id:94168)

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Face recognition has been deployed in a wide range of important applications including surveillance and forensic identification. However, it still seems to be a challenging problem as its performance severely degrades under illumination, pose and expression variations, as well as with occlusions, and aging. In this thesis, we have investigated the use of local facial skin data as a source of biometric information to improve human recognition. Skin texture features have been exploited in three major tasks, which include (i) improving the performance of conventional face recognition systems, (ii) building an adaptive skin-based face recognition system, and (iii) dealing with circumstances when a full view of the face may not be avai'lable. Additionally, a fully automated scheme is presented for localizing eyes and mouth and segmenting four facial regions: forehead, right cheek, left cheek and chin. These four regions are divided into nonoverlapping patches with equal size. A novel skin/non-skin classifier is proposed for detecting patches containing only skin texture and therefore detecting the pure-skin regions. Experiments using the XM2VTS database indicate that the forehead region has the most significant biometric information. The use of forehead texture features improves the rank-l identification of Eigenfaces system from 77.63% to 84.07%. The rank-l identification is equal 93.56% when this region is fused with Kernel Direct Discriminant Analysis algorithm.

Item Type: Thesis (Doctor of Philosophy (PhD))
Thesis advisor: Deravi, Farzin
DOI/Identification number: 10.22024/UniKent/01.02.94168
Additional information: This thesis has been digitised by EThOS, the British Library digitisation service, for purposes of preservation and dissemination. It was uploaded to KAR on 25 April 2022 in order to hold its content and record within University of Kent systems. It is available Open Access using a Creative Commons Attribution, Non-commercial, No Derivatives ( licence so that the thesis and its author, can benefit from opportunities for increased readership and citation. This was done in line with University of Kent policies ( If you feel that your rights are compromised by open access to this thesis, or if you would like more information about its availability, please contact us at and we will seriously consider your claim under the terms of our Take-Down Policy (
Subjects: T Technology
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts
SWORD Depositor: SWORD Copy
Depositing User: SWORD Copy
Date Deposited: 27 Sep 2022 14:43 UTC
Last Modified: 27 Sep 2022 14:43 UTC
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