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Fusion Classifier for Open-Set Face Recognition with Pose Variations

Main Author: Gee-Sern Jison Hsu
Format: Article eJournal
Bahasa: eng
Terbitan: , 2009
Subjects:
Face recognition
open-set identification
hidden Markov model
support vector machines
Online Access: https://zenodo.org/record/1061412
ctrlnum 1061412
fullrecord <?xml version="1.0"?> <dc schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"><creator>Gee-Sern Jison Hsu</creator><date>2009-08-21</date><description>A fusion classifier composed of two modules, one made by a hidden Markov model (HMM) and the other by a support vector machine (SVM), is proposed to recognize faces with pose variations in open-set recognition settings. The HMM module captures the evolution of facial features across a subject-s face using the subject-s facial images only, without referencing to the faces of others. Because of the captured evolutionary process of facial features, the HMM module retains certain robustness against pose variations, yielding low false rejection rates (FRR) for recognizing faces across poses. This is, however, on the price of poor false acceptance rates (FAR) when recognizing other faces because it is built upon withinclass samples only. The SVM module in the proposed model is developed following a special design able to substantially diminish the FAR and further lower down the FRR. The proposed fusion classifier has been evaluated in performance using the CMU PIE database, and proven effective for open-set face recognition with pose variations. Experiments have also shown that it outperforms the face classifier made by HMM or SVM alone.</description><identifier>https://zenodo.org/record/1061412</identifier><identifier>10.5281/zenodo.1061412</identifier><identifier>oai:zenodo.org:1061412</identifier><language>eng</language><relation>doi:10.5281/zenodo.1061411</relation><relation>url:https://zenodo.org/communities/waset</relation><rights>info:eu-repo/semantics/openAccess</rights><rights>https://creativecommons.org/licenses/by/4.0/legalcode</rights><subject>Face recognition</subject><subject>open-set identification</subject><subject>hidden Markov model</subject><subject>support vector machines.</subject><title>Fusion Classifier for Open-Set Face Recognition with Pose Variations</title><type>Journal:Article</type><type>Journal:Article</type><recordID>1061412</recordID></dc>
language eng
format Journal:Article
Journal
Journal:eJournal
author Gee-Sern Jison Hsu
title Fusion Classifier for Open-Set Face Recognition with Pose Variations
publishDate 2009
topic Face recognition
open-set identification
hidden Markov model
support vector machines
url https://zenodo.org/record/1061412
contents A fusion classifier composed of two modules, one made by a hidden Markov model (HMM) and the other by a support vector machine (SVM), is proposed to recognize faces with pose variations in open-set recognition settings. The HMM module captures the evolution of facial features across a subject-s face using the subject-s facial images only, without referencing to the faces of others. Because of the captured evolutionary process of facial features, the HMM module retains certain robustness against pose variations, yielding low false rejection rates (FRR) for recognizing faces across poses. This is, however, on the price of poor false acceptance rates (FAR) when recognizing other faces because it is built upon withinclass samples only. The SVM module in the proposed model is developed following a special design able to substantially diminish the FAR and further lower down the FRR. The proposed fusion classifier has been evaluated in performance using the CMU PIE database, and proven effective for open-set face recognition with pose variations. Experiments have also shown that it outperforms the face classifier made by HMM or SVM alone.
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