A novel two-stage illumination estimation framework for expression recognition.
One of the critical issues for facial expression recognition is to eliminate the negative effect caused by variant poses and illuminations. In this paper a two-stage illumination estimation framework is proposed based on three-dimensional representative face and clustering, which can estimate illumi...
| Publicado en: | Scientific World Journal pp. 565389 - 565390 |
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| Autores principales: | , , |
| Formato: | research Journal Article |
| Publicado: |
Wiley-Blackwell
2014
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=109753053&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 109753053 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 1537744X 1BX5 jtl: Scientific World Journal issn: 1537744X maglogo: N pubinfo: dt: 2014 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 109753053 109753053 NLM24977212 2012633816 10.1155/2014/565389 NLM24977212 PMC4009132 109753053 ppf: 565389 ppct: 1 formats: tig: atl: A novel two-stage illumination estimation framework for expression recognition. aug: au: Zhang, Zheng Song, Guozhi Wu, Jigang affil: School of Computer Science and Software Engineering, Tianjin Polytechnic University, Tianjin 300387, China. sug: subj: Face Anatomy and Histology Facial Expression Image Interpretation, Computer Assisted Methods Imaging, Three-Dimensional Methods Lighting Methods Information Science Methods Photography Methods Algorithms Biometrics Methods ab: One of the critical issues for facial expression recognition is to eliminate the negative effect caused by variant poses and illuminations. In this paper a two-stage illumination estimation framework is proposed based on three-dimensional representative face and clustering, which can estimate illumination directions under a series of poses. First, 256 training 3D face models are adaptively categorized into a certain amount of facial structure types by k-means clustering to group people with similar facial appearance into clusters. Then the representative face of each cluster is generated to represent the facial appearance type of that cluster. Our training set is obtained by rotating all representative faces to a certain pose, illuminating them with a series of different illumination conditions, and then projecting them into two-dimensional images. Finally the saltire-over-cross feature is selected to train a group of SVM classifiers and satisfactory performance is achieved when estimating a number of test sets including images generated from 64 3D face models kept for testing, CAS-PEAL face database, CMU PIE database, and a small test set created by ourselves. Compared with other related works, our method is subject independent and has less computational complexity O(C × N) without 3D facial reconstruction. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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