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http://dx.doi.org/10.3745/KIPSTB.2008.15-B.5.477

Improvement of Face Recognition Rate by Normalization of Facial Expression  

Kim, Jin-Ok (대구한의대학교 정보경영대학 모바일콘텐츠학부)
Abstract
Facial expression, which changes face geometry, usually has an adverse effect on the performance of a face recognition system. To improve the face recognition rate, we propose a normalization method of facial expression to diminish the difference of facial expression between probe and gallery faces. Two approaches are used to facial expression modeling and normalization from single still images using a generic facial muscle model without the need of large image databases. The first approach estimates the geometry parameters of linear muscle models to obtain a biologically inspired model of the facial expression which may be changed intuitively afterwards. The second approach uses RBF(Radial Basis Function) based interpolation and warping to normalize the facial muscle model as unexpressed face according to the given expression. As a preprocessing stage for face recognition, these approach could achieve significantly higher recognition rates than in the un-normalized case based on the eigenface approach, local binary patterns and a grey-scale correlation measure.
Keywords
Facial expression recognition; Face recognition; Expression normalization; RBF(Radial Basis Function);
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Times Cited By KSCI : 1  (Citation Analysis)
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1 Pillips P. J., Flynn P. J., Scruggs T., Bowyer K., Chang J., Hoffman K., Marques J., Min J. and Worek W., “Overview of the Face Recognition Grand Challenge,” 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol.1, pp.947-954, 2005   DOI
2 Hay D., Young A. and Ellis A, “Routes through the Face Recognition System,” Journal of Exp. Psychol A-Human Exp., Vol.43, pp.761-791, 1991   DOI   ScienceOn
3 Cootes T. and Taylor C., “Anatomical Statistical Models and their Role in Feature Extraction,” British Journal of Radiology, Vol.77, pp.133-S139, 2004   DOI   ScienceOn
4 신기한, 전준철, “동영상 기반 얼굴 애니메이션 콘텐츠 제작 기술”, 한국인터넷정보학회지, 8권 4호, pp.44-53, 2007
5 Li C. and Barreto A., “Biometric Recognition of 3D Faces and Expressions,” Lecture Notes in Computer Science, Vol. 2688, pp.62-70, 2003
6 Turk M. and Pentland A., “Eigenfaces for Recognition,” Journal of Cognitive Neuroscience, Vol.3, No.7, pp.71-86, 1991   DOI   ScienceOn
7 Platt S. M. and Badler N. I., “Animating Facial Expressions,” Proceedings of 8th Annual Conference on Computer Graphics and Interactive Technique, pp.245-252, 1981   DOI
8 M. Pantic and L. J. M. Rothkrantz, “Expert System for Automatic Analysis of Facial Expressions,” Image and Vision Computing, Vol.18, pp.881-905, 2000   DOI   ScienceOn
9 Ahonen T., Hadid A. and Pietikainen M., “Face Recognition with Local Binary Patterns,” Proc. of the 8th ECCV(European Conference on Computer Vision), pp.469-481, 2004
10 Bronstein A. M., Bronstein M. M. and Kimmel R., “Expression-Invariant 3D Face Recognition,” Lecture Notes in Computer Science, Vol.2688, pp.62-69, Springer, 2003
11 Ekman P., “Emotion in the Human Face,” Cambridge University Press, 1982
12 Jiang D., Hu Y., Yan S., Zhang L., Zhang H. and Gao W., “Efficient 3D Reconstruction for Face Recognition,” Pattern Recognition, Vol.38, No.6, pp.787-798, Elsevier, 2004   DOI   ScienceOn
13 Cootes T., Edwards G. J. and Taylor C., “Active Appearance Models,” IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol.23, pp.681-685, 2001   DOI   ScienceOn
14 S. Gundimada, L. Tao and V. Asari, “Face Detection Technique based on Intensity and Skin Color Distribution,” International Conference on Image Processing, Vol.2, pp.1413-1416, 2004   DOI
15 김진옥, “색상조합모델과 LM(Levenberg-Marquadt)알고리즘을 이용한 얼굴 영역 검출”, 한국정보처리학회 논문지 B, 14-B권, 4호, pp.255-262, 2007   DOI
16 Waters K., ”A Muscle Model for Animating three-dimensional Facial Expressions, Proceedings of SIGGRAPH87, Vol.21, pp.17-24, 1987   DOI
17 Vetter T. and Poggio T., “Linear Object Classes and Image Synthesis from a Single Example Image,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.19, pp.733-742, 1997   DOI   ScienceOn