A Proposal to increase by genetic algorithm the discriminatory
Main Article Content
Keywords
Faces recognition, infrared images, genetic algorithm, Principal
Abstract
PCA and LDA are two of most widely used techniques for face recognition with IR images. In this paper we report the results obtained by using Genetics Algorithms for optimization the characteristic vector generated by these techniques, by assignation of weights to each vector according its performance in the classification task. It shows that, under the proposed scheme, is able to obtain a lower classification error compared to conventional method.
PACS: 07.05.Pj, 07.57.Kp, 85.25.Pb,
MSC: 68T05
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References
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[3] Lawrence B. Wolff, Diego A. Socolinsky, and Christopher K. Eveland. Quantitative Measurement of Illumination Invariance for Face Recognition Using Thermal Infrared Imagery. Proceedings of IEEE Workshop on Computer Vision Beyond the Visible Spectrum: Methods and Applications, ISBN 0-7695- 0640-2, 4820, 140–151 (2003).
[4] G. Friedrich and Y. Yeshurun. Seeing People in the Dark: Face Recognition in Infrared Images. Proceedings of Second International Workshop on Biologically Motivated Computer Vision, ISBN:3-540-00174-3, Vol. 2525, 348–359 (2002).
[5] Xin Chen, Patrick J. Flynn and Kevin W. Bowyer. IR and visible light face recognition. Computer Vision and Image Understanding, ISSN: 1077-3142, 9(3), (2005).
[6] Lawrence B. Wolff, Diego A. Socolinsky. Thermal Face Recognition in an Operational Scenario. Computer Vision and Pattern Recognition. CVPR 2004, ISSN: 1063-6919, Vol. 2 ,1012–1019 (2004).
[7] Diego A. Socolinsky and Selinger A.; . Thermal Face Recognition Over Time. Proceedings of the 17th International Conference on Pattern Recognition, ISSN: 1051-4651, Vol. 4, 187–194 (2004).
[8] Li S.Z, RuFeng Chu, ShengCai Liao and Lun Zhang. Illumination Invariant Face Recognition Using Near-Infrared Images. Transactions on Pattern Analysis and Machine Intelligence, ISSN 0162-8828, 29(4), 627–639 (2007).
[9] P. Buddharaju, I.T. Pavlidis, P. Tsiamyrtzis and M. Bazakos. Physiology-based face recognition in the thermal infrared spectrum. IEEE Transactions on Pattern Analysis and Machine Intelligence, ISSN 0162- 8828, 29(4), 613–626 (2007).
[10] ShahbeM. Desa and Subhas Hati. IR and Visible Face Recognition using Fusion of Kernel Based Features. 19thInternational Conference on Pattern Recognition. ICPR 2008, ISSN 1051–4651, pp. 1–4 (2008).
[11] Mrinal Kanti Bhowmik, Debotosh Bhattacharjee, Mita Nasipuri, Dipak Kumar Basu and Mahantapas Kundu. Classificationof Polar-Thermal Eigenfaces using
Multilayer Perceptron for Human Face Recognition. Third international Conference on Industrial and Information Systems, 2008. ICIIS 2008, ISBN: 978-1-4244-2806-9, pp. 1–6 (2008).
[12] Moulay A. Akhloufi, and Abdelhakim Bendada. Infrared face recognition using distance transforms. International Conference on Image and Vision Computing, Paris, France,Proceedings of World Academy of Science, Engineering and Technology,ISSN 1307–6884, Vol. 30, pp. 160–163 (2008).
[13] Moulay A. Akhloufi and Abdelhakim Bendada. Probabilistic Bayesian framework for infrared face recognition. World Academy of Science, Engineering and Technology, ISSN 2010-376X, (2009).
[14] Zhihua Xie, Guodon Liu, Shiqian Wu and Yu Lu. A Fast Infrared Face Recognition System Using Curvelet Transformation. Second International Symposium on Electronic Commerce and Security, 2009. ISECS ’09, ISBN: 978-0-7695-3643-9, Vol. 2, 145–149 (2009).
[15] Xie, Shiqian Wu, Guodong Liu and Zhijun Fang. Infrared Face Recognition Based on Radiant Energy and Curvelet Transformation. Fifth International Conference on Information Assurance and Security 2009, ISBN: 978-0-7695-3744-3, (2009).
[16] Xie, Shiqian Wu, Guodong Liu, Zhijun Fang. Infrared face recognition method based on blood perfusion image and Curvelet transformation. Proceedings of the 2009 International Conference on Wavelet Analysis and Pattern Recognition, ICWAPR 2009, ISBN: 978-1-4244-3728-3, pp. 360–364 (2009).
[17] Siu-Yeung Cho, Lingyu Wang and Wen Jin Ong. Thermal Imprint Feature Analysis for Face Recognition. IEEE International Symposium on Industrial Electronics (ISlE 2009), ISBN: 978-1-4244-4347-5, pp. 1875–1880 (2009).
[18] Wei Ge, Dawei Wang, and Yuqi Cheng. Infrared face recognition using linear subspace analysis . Proceedings of the SPIE on Pattern Recognition and Computer Vision, ISBN 978-0-8194-7807-8, Vol. 7496, pp. 74961Z–74961Z-8 (2009).
[19] Moulay A. Akhloufi and Abdelhakim Bendada. Infrared face recognition using texture descriptors. Proc. SPIE, Vol. 7661, 766109, ISBN: 9780819481252, (2010).
[20] Zhaojun Xue, Dong Ming, Wei Song, Baikun Wan and Shijiu Jin. Infrared gait recognition based on wavelet transform and support vector machine. Pattern Recognition, ISSN: 0031-3203, 43(8), 2904–2910 (2010).
[21] Matthew Turk and Alex Pentland. Eigenfaces for Recognition. Journal of Cognitive Neuroscience Winter 1991, ISSN 0898–929X, Vol. 3, 71–86 (1991).
[22] Juwei Lu, Plataniotis K.N and Venetsanopoulos A.N. Face recognition using LDA-based algorithms. IEEE Transactions on Neural Networks, ISSN: 1045– 9227, 14(1), 195–200 (2001).
[23] M. Brand. Fast low-rank modifications of the thin singular value decomposition. Algebra and Its Applications, ISSN 0024–3795, Vol. 415, 20–30 (2006).
[24] Martínez T, D. Estudio Comparativo entre Diferentes Técnicas de Extracción de Características Aplicadas al Reconocimiento de Rostros Empleando Imágenes Infrarrojas. Trabajo de grado de Maestría presentado a la Universidad del Valle, (2007).
[25] De La Cruz C, Patino H.D, and Carelli R. New Evolutionary Algorithm based on the Mathematical Modeling of the Evolution of a Species. IEEE Latin America Transactions, ISBN: 0-7803-9487-9, 3(4), 310–316 (2005).