<?xml version="1.0" encoding="UTF-8"?> <article key="pdf/5636" mdate="2009-08-21 00:00:00"> <author>Gee-Sern Jison Hsu</author> <title>Fusion Classifier for OpenSet Face Recognition with Pose Variations</title> <pages>2099 - 2105</pages> <year>2009</year> <volume>3</volume> <number>8</number> <journal>International Journal of Computer and Information Engineering</journal> <ee>https://publications.waset.org/pdf/5636</ee> <url>https://publications.waset.org/vol/32</url> <publisher>World Academy of Science, Engineering and Technology</publisher> <abstract>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 openset recognition settings. The HMM module captures the evolution of facial features across a subjects face using the subjects 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 openset face recognition with pose variations. Experiments have also shown that it outperforms the face classifier made by HMM or SVM alone. </abstract> <index>Open Science Index 32, 2009</index> </article>