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Zineb Elgarrai
FST of Hassan 1st University Settat, Morocco
Computer Science & IT Electrical & Electronics Engineering

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Unimodal Multi-Feature Fusion and one-dimensional Hidden Markov Models for Low-Resolution Face Recognition Othmane El Meslouhi; Zineb Elgarrai; Mustapha Kardouchi; Hakim Allali
International Journal of Electrical and Computer Engineering (IJECE) Vol 7, No 4: August 2017
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (706.615 KB) | DOI: 10.11591/ijece.v7i4.pp1915-1922

Abstract

The objective of low-resolution face recognition is to identify faces from small size or poor quality images with varying pose, illumination, expression, etc. In this work, we propose a robust low face recognition technique based on one-dimensional Hidden Markov Models. Features of each facial image are extracted using three steps: firstly, both Gabor filters and Histogram of Oriented Gradients (HOG) descriptor are calculated. Secondly, the size of these features is reduced using the Linear Discriminant Analysis (LDA) method in order to remove redundant information. Finally, the reduced features are combined using Canonical Correlation Analysis (CCA) method. Unlike existing techniques using HMMs, in which authors consider each state to represent one facial region (eyes, nose, mouth, etc), the proposed system employs 1D-HMMs without any prior knowledge about the localization of interest regions in the facial image. Performance of the proposed method will be measured using the AR database.
Co-Authors Hakim Allali Mustapha Kardouchi Othmane El Meslouhi