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Multi-Pose Face Recognition Using Pairwise Supervised Dictionary Learning
Volume 30, Issue 4 (2019), pp. 647–670
Pub. online: 1 January 2019      Type: Research Article      Open accessOpen Access
Received
1 March 2018
Accepted
1 April 2019
Published
1 January 2019

Abstract

A major challenge in face recognition is handling large pose variations. Here, we proposed to tackle this challenge by a three step sparse representation based method: estimating the pose of an unseen non-frontal face image, generating its virtual frontal view using learned view-dependent dictionaries, and classifying the generated frontal view. It is assumed that for a specific identity, the representation coefficients based on the view dictionary are invariant to pose and view-dependent frontal view generation transformations are learned based on pair-wise supervised dictionary learning. Experiments conducted on FERET and CMU-PIE face databases depict the efficacy of the proposed method.

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Biographies

Farahani Ali
cpt.mazi@gmail.com

A. Farahani received his BSc from Arak University, Arak, Iran, in software engineering in 2014. Then he pursued his MSc in artificial intelligence in Shahid Bahonar University of Kerman, Iran, and received his master degree in 2017 under the supervision of Dr Hadis Mohseni. His research interests include pattern recognition, supervised and unsupervised learning methods and their applications in image processing.

Mohseni Hadis
hmohseni@uk.ac.ir

H. Mohseni received his BSc in hardware engineering from Sharif University of Technology (SUT), Tehran, Iran, in 2004. Then she continued her MSc in artificial intelligence in SUT and received her degree in 2007 working on medical image processing. She then pursued her PhD in artificial intelligence in SUT working on multi-pose face recognition and received her PhD degree in 2013 under the supervision of Prof. Shohreh Kasaei. Now she is an assistant professor in Shahid Bahonar University of Kerman and her research interests include pattern recognition, image and video processing, medical image processing and deep learning.


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© 2019 Vilnius University
Open access article under the CC BY license.

Keywords
face recognition multi-pose sparse representation supervised dictionary learning

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