Title: Classification and gender recognition from veiled-faces

Authors: Ahmad B. Hassanat; V.B. Surya Prasath; Bassam M. Al-Mahadeen; Samaher Madallah Moslem Alhasanat

Addresses: Department of Information Technology Mutah University, Karak, Jordan ' Computational Imaging and VisAnalysis (CIVA) Lab, Department of Computer Science, University of Missouri-Columbia, USA ' Department of Information Technology, Mutah University, Karak, Jordan ' Department of Information Technology Mutah University, Karak, Jordan

Abstract: This study aims to investigate to what extent a computer system can identify veiled-human and recognise gender using eyes and the uncovered part of the face. For the purpose of this study, we have created a new veiled persons image (VPI) database shot using a mobile phone camera, imaging 100 different veiled-persons over two sessions. After preprocessing and segmentation we used a fused method for feature extraction. The fusion occurs between geometrical (edge ratio) and textural (probability density function of the colour moments) features. The experimental results using different classifiers were ranging from 88:63% to 97:22% for person identification accuracy before feature selection and up to 97:55% after feature selection. The proposed method achieved up to 99:41% success rate for gender classification.

Keywords: face recognition; biometrics; veiled faces; features fusion.

DOI: 10.1504/IJBM.2017.088251

International Journal of Biometrics, 2017 Vol.9 No.4, pp.347 - 364

Received: 20 Dec 2016
Accepted: 04 Sep 2017
Published online: 30 Nov 2017 *

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