Una propuesta para incrementar la capacidad discriminante de las técnicas PCA y LDA aplicadas al reconocimiento de rostros con imágenes IR
Main Article Content
Keywords
Reconocimiento de rostros, imágenes infrarrojas, Algoritmos genéticos, Análisis de Componentes Principales, Análisis Discriminante Lineal.
Resumen
Dos de las técnicas más ampliamente utilizadas en el campo del reconocimiento de rostros con imágenes infrarrojas son PCA (Principal Component Analisys) y LDA (Linear Discriminant Analysis). En este trabajo se presentan los resultados obtenidos al emplear algoritmos genéticos para incrementar el poder discriminante de los vectores que conforman el espacio de características generado por dichas técnicas, por medio de la asignación ponderada de pesos a cada vector según su nivel de aporte en la etapa de clasificación. Se muestra que bajo el esquema propuesto, se obtiene un menor error de clasificación respecto al método convencional.
PACS: 07.05.Pj, 07.57.Kp, 85.25.Pb,
MSC: 68T05
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Referencias
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[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).
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[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).
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[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).
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[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).