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A Unified Framework for Head Pose, Age and Gender Classification through End-to-End Face Segmentation

1
Department of Electrical Engineering, University of Azad Jammu and Kashmir, Muzafarabbad 13100, Pakistan
2
Department of Software Engineering, Sejong University, Seoul 05006, Korea
3
Department of Software Engineering, University of Azad Jammu and Kashmir, Muzafarabbad 13100, Pakistan
4
Dipartimento di Elettronica e Telecomunicazioni (DET), Politecnico di Torino, 10156 Torino, Italy
5
IT Department, College of Computer, Qassim University, Al-Mulida 51431, Saudi Arabia
*
Authors to whom correspondence should be addressed.
Entropy 2019, 21(7), 647; https://doi.org/10.3390/e21070647
Received: 2 June 2019 / Revised: 23 June 2019 / Accepted: 24 June 2019 / Published: 30 June 2019
(This article belongs to the Special Issue Statistical Machine Learning for Human Behaviour Analysis)
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Abstract

Accurate face segmentation strongly benefits the human face image analysis problem. In this paper we propose a unified framework for face image analysis through end-to-end semantic face segmentation. The proposed framework contains a set of stack components for face understanding, which includes head pose estimation, age classification, and gender recognition. A manually labeled face data-set is used for training the Conditional Random Fields (CRFs) based segmentation model. A multi-class face segmentation framework developed through CRFs segments a facial image into six parts. The probabilistic classification strategy is used, and probability maps are generated for each class. The probability maps are used as features descriptors and a Random Decision Forest (RDF) classifier is modeled for each task (head pose, age, and gender). We assess the performance of the proposed framework on several data-sets and report better results as compared to the previously reported results. View Full-Text
Keywords: face analysis; face segmentation; head pose estimation; age classification; gender classification face analysis; face segmentation; head pose estimation; age classification; gender classification
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Khan, K.; Attique, M.; Syed, I.; Sarwar, G.; Irfan, M.A.; Khan, R.U. A Unified Framework for Head Pose, Age and Gender Classification through End-to-End Face Segmentation. Entropy 2019, 21, 647.

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