Browse > Article
http://dx.doi.org/10.5391/JKIIS.2015.25.5.437

Design of an observer-based decentralized fuzzy controller for discrete-time interconnected fuzzy systems  

Kong, Seong G. (Department of Computer Engineering, Sejong University)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.25, no.5, 2015 , pp. 437-443 More about this Journal
Abstract
This paper presents face recognition based on the fusion of visible image and thermal infrared (IR) texture estimated from the face image in the visible spectrum. The proposed face recognition scheme uses a multi- layer neural network to estimate thermal texture from visible imagery. In the training process, a set of visible and thermal IR image pairs are used to determine the parameters of the neural network to learn a complex mapping from a visible image to its thermal texture in the low-dimensional feature space. The trained neural network estimates the principal components of the thermal texture corresponding to the input visible image. Extensive experiments on face recognition were performed using two popular face recognition algorithms, Eigenfaces and Fisherfaces for NIST/Equinox database for benchmarking. The fusion of visible image and thermal IR texture demonstrated improved face recognition accuracies over conventional face recognition in terms of receiver operating characteristics (ROC) as well as first matching performances.
Keywords
Face Recognition; Thermal IR Image; Data Fusion; Illumination Variations; Neural Networks;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 W. Zhao, R. Chellappa, P. J. Phillips, and A. Rosenfeld, "Face recognition: A literature survey," ACM Computing Surveys, Vol. 35, No. 4, pp.399-458, 2003.   DOI
2 I. Pavlidis and P. Symosek, "The imaging issue in an automatic face/disguise detection system," Proc. IEEE Workshop on Computer Vision Beyond the Visible Spectrum, pp.15-24, 2000.
3 S. G. Kong, J. Heo, B. R. Abidi, J. Paik, and M. A. Abidi, "Recent advances in visual and infrared face recognition - A review," Computer Vision and Image Understanding, Vol. 97, No. 1, pp.103-135, 2005.   DOI
4 S. G. Kong, J. Heo, F. Boughorbel, Y. Zheng, B. R. Abidi, A. Koschan, M. Yi, and M. A. Abidi, "Multiscale fusion of visible and thermal IR images for illumination-invariant face recognition," International Journal of Computer Vision, Vol. 71, No. 2, pp.215-233, 2007.   DOI
5 G. Bebis, A. Gyaourova, S. Singh, and I. Pavlidis, "Face recognition by fusing thermal infrared and visible imagery," Image and Vision Computing, Vol. 24, No. 7, pp.727-742, 2006.   DOI
6 M. Reiter, R. Donner, G. Langs, and H. Bischof, "3D and Infrared Face Reconstruction from RGB data using Canonical Correlation Analysis," Proc. Int. Conf. Pattern Recognition, Vol. 1, pp.425-428, 2006.
7 J. Li, P. Hao, C. Zhang, and M. Dou, "Hallucinating faces from thermal infrared images," Proc. IEEE Int. Conf. Image Processing, pp.465-468, 2008.
8 A. C. S. Chung and H. C. Shen, "Entropy-based Markov chains for multisensor fusion," Journal of Intelligent and Robotic Systems, Vol. 29, No. 2, pp.161-189, 2000.   DOI
9 K. Hornik, M. Stinchcombe, and H. White, "Multilayer feedforward networks are universal approximators," Neural Networks, Vol. 2, No. 5, pp.359-366, 1989.   DOI
10 M. Turk and A. Pentland, "Eigenfaces for recognition," J. Cognitive Neuroscience, Vol. 3, No. 1, pp.71-86, 1991.   DOI   ScienceOn
11 J. S. Park, Y. H. Oh, S. C. Ahn, and S.-W. Lee, "Glasses removal from facial image using recursive error compensation," IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 27, No. 5, pp.805-811, 2005.   DOI
12 Y. S. Russell and C. Eberhart, "Particle swarm optimization: developments, applications and resources," Proc. 2001 Congress on Evolutionary Computation, Vol. 1, pp.81-86, 2001.
13 P. N. Belhumeur, J. P. Hespanha, and D. J. Kriegman, "Eigenfaces vs. Fisherfaces: recognition using class specific linear projection," IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 19, No. 7, pp.711-720, 1997.   DOI
14 J. Lu, K. N. Plataniotis, and A. N. Venetsano-poulos, "Face recognition using kernel direct discriminant analysis algorithms," IEEE Trans. on Neural Networks, Vol. 14, No. 1, pp.117-126, 2003.   DOI
15 X. He, S. Yan, Y. Hu, P. Niyogi, and H.-J. Zhang, "Face recognition using Laplacianfaces," IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 27, No. 3, pp.328-340, 2005.   DOI   ScienceOn
16 http://www.equinoxsensors.com/products/HID.html
17 S. Yeom, "Multi-frame Face Classification with Decision Level Fusion based on Photon Counting," Int'l Journal of Fuzzy Logic and Intelligent Systems, Vol. 14, No. 4, pp.332-339, Dec. 2014.   DOI
18 A. S. Georghiades, P. N. Belhumeur, and D. J. Kriegman, "From few to many: illumination cone models for face recognition under variable lighting and pose," IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 23, No. 6, pp.643-660, 2001.   DOI
19 K. C. Lee, J. Ho, and D. J. Kriegman, "Acquiring linear subspaces for face recognition under variable lighting," IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 27, No. 5, pp.684-698, 2005.   DOI   ScienceOn
20 J. R. Beveridge, D. Bolme, B. A. Draper, and M. Teixeira, "The CSU face identification evaluation system," Machine Vision and Applications, Vol. 16, No. 2, pp.128-138, 2005.   DOI