Artículos
Publicado 2011-12-15
Palabras clave
- Análisis de Componentes Principales (PCA),
- eigenfaces,
- AdaBoost,
- distancia euclidiana,
- distancia mahalanobis
Cómo citar
Pedraza Pico, B. O., Rondón, P., & Arguello, H. (2011). Sistema de reconocimiento facial basado en imágenes con color. Revista UIS Ingenierías, 10(2), 113–122. Recuperado a partir de https://revistas.uis.edu.co/index.php/revistauisingenierias/article/view/113-122
Resumen
En este trabajo se desarrolla un sistema algorítmico con el fin de comprobar si el papel del color puede ser un atributo importante en los sistemas de reconocimiento facial en dos dimensiones (2-D), con orientación frontal y pequeñas variaciones en los gestos de los individuos. La primera fase consiste en la detección y localización del rostro humano para la cual se emplea el algoritmo de aprendizaje AdaBoost y una combinación de clasificadores en cascada, con el fin de aumentar las tasas de detección. En una segunda fase se aplica el enfoque de eigenfaces y se implementa un sistema clasificador para reconocer e identificar el sujeto de entrada a un individuo específico, utilizando la distancia euclidiana y de mahalanobis. Se ilustran los resultados obtenidos del sistema propuesto tanto para imágenes en color como en grises, contrastando que la información del color en el plano HSV puede mejorar las tasas de reconocimiento cuando se compara con el plano RGB.
Descargas
Los datos de descargas todavía no están disponibles.
Referencias
- E. Hjelmas and B.K. Low, “Face detection: A survey” Computer Vision and Image Understanding, September 2001 pp. 236-274.
- M. H. Yang, D. J. Kriegman, and N. Ahuja, “Detecting faces in images: A survey.” IEEE Transactions on pattern analysis and machine intelligence, Vol. 24, No. 1, 2002, pp.34-58.
- L. Lorente and L. Torres, “A global eigen approach for face recognition.” International Workshop on Very Low Bit-rate Video Coding, Urbana, Illinois, USA, October 8-9 1998.
- “Special issue on face and gesture recognition” IEEE Transactions on Pattern Analysis and Machine Intelligence, July 1997, vol. 19.
- O.J. Hernandez and M. S. Kleiman, “Face recognition using multispectral random field texture models, color content, and biometric features”. Applied Image Pattern Recognition Workshop, 2005, pp.204-209.
- S. B. Lee and S. tsutsui, “Intelligent biometric techniques in fingerprint and face recognition”. Boca Raton, FL, USA: CRC Press, Inc., 1999.
- J. Fabregas and M. Faundez-Zanuy, “Biometric face recognition with different training and testing databases”. in Verbal and Nonverbal Features of Human-Human and Human-Machine Interaction (A.Esposito, N.Bourbakis, N.Avouris, and I. Hatzilygeroudis, eds.), vol. 5042 of Lecture Notes in Computer Science, Springer Berlin Heidelberg, 2008, pp. 44—55.
- E. Hjelmas, “Biometric Systems: A Face Recognition Approach”, 2000.
- J. Abbazio, S. Perez, D. Silva, R. Tesoriero, F. Panna, and R. Zack, “Face biometric systems”. Proceeding of Strudent-Faculty Research Day, CSIS, Pace University, Mayo 8th 2009.
- S. M. V. Palacios, “Sistema de reconocimiento de reconocimiento de rostros”. Universidad Peruana de Ciencias Aplicadas (UPC).
- R. C. González and R. E. Woods, “Digital Image Processing”, 3rd ed. Upper Saddle River, NJ, USA: Prentice-Hall, Inc., 2006.
- J. C. Russ, “The image processing handbook”, 3rd ed Boca Raton, FL, USA: CRC Press, Inc., 1999.
- A. Rama and F. Tarrés, Un nuevo método para la detección de caras basado en integrales difusas. Universidad Politécnica de Catalunya, Barcelona, España.
- P. Viola and M. J. Jones, Robust real-time face detection. Int. J.Comput. Vision, vol.57, May 2004, pp.137-154.
- R. Lienhart, A. Kuranov, and V. Pisarevsky, “Empirical analysis of detection cascades of boosted classifiers for rapid object detection”. in Pattern Recognition of Lecture Notes in Computer Science, Springer Berlin / Heidelberg, 2003, vol. 2781, pp. 297-304.
- H. Masnadi-Shirazi, “Adaboost face detection”. Department of Electoral and Computer Engineering at the University of California, San Diego.
- R. E. Schapire, Y. Freund, P. Bartlett, and W. S. Lee, “Boosting the margin: A new explanation for the effectiveness of voting methods”. The Annals of Statistics, 1997, vol. 26, pp. 322-330.
- R. E. Schapire, Y. Freund, P. Bartlett, and W. S. Lee, “Boosting the margin: A new explanation for the effectiveness of voting methods”. The Annals of Statistics, 1998, vol. 26, No. 5, pp. 1651-1686.
- An extended set of Haar-like features for rapid object detection. 2002, vol. 1.
- E. Osuna, R. Freund, and F. Girosit, “Training support vector machines: an application to face detection”. in Computer Vision and Pattern Recognition, Proceedings, IEEE Computer Society Conference, June 1997, pp. 130-136.
- C. P. Papageorgiou, M. Oren, and T. Poggio, “A general framework for object detection”. Computer Vision, IEEE International Conference on, 1998, p. ~555.
- E. E. Osuna, R. Freund, and F. Girosi, “Support vector machines: Training and applications”. 1997.
- S. Han, Y. Han, and H. Hahn, “Vehicle on significance of color in face recognition using several eigenface algorithms”. World Academy of Science, Engineering and Technology, 2009.
- B. Karimi and A. Krzyzak, “A study on significance of color in face recognition using several eigenface algorithms”. in Electrical and Computer Engineering, CCECE 2007. Canadian Conference on, pp. 1309-1312.
- L. Torres, J. Reutter, and L. Lorente, “The importance of the color information in face recognition”. Image Processing, ICIP99, Proceedings. International Conference, 1999, vol. 3, pp. 627-631.
- A. Yip and P. Sinha, “Role of color in face recognition”. AIM-2001-035, CBCL-212, December 2001.
- X. Yu and G. Baciu, Face recognition from color images in presence of dynamic orientations and illumination conditions in Biometric Authentication (D. Zhang and A. K. Jain, eds.), of Lecture Notes in Computer Science, Springer Berlin Heidelberg, 2004, vol. 3072, pp. 1-21.
- M. J. Swain and D. H. Ballard, “Color indexing”. International Journal of Computer Vision, 1991, vol. 7, pp. 11-32.
- J. Lu, K. Plataniotis, and A. Venetsanopoulos, “Face recognition using lda-based algorithms”. Neural Networks, IEEE Transactions, Jan. 2003, vol. 14, pp. 195 – 200.
- M. Bartlett, J. Movellan, and T. Sejnowski, “Face recognition by independent component analysis”. Neural Networks, IEEE Transactions on, Nov. 2002, vol. 13, pp. 1450 - 1464.
- H. Cevikalp, M. Neamtu, M. Wilkes, and A. Barkana, “Discriminative common vectors for face recognition”. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, vol. 27, pp. 4-13.
- X. He, S. Yan, Y. Hu, P. Niyogi, and H.-J. Zhang, “Face recognition using laplacianfaces”. Pattern Analysis and Machine Intelligence, IEEE Transactions, 2005, vol. 27, No. 3, pp. 328 -340.
- Z. Liu and C. Liu, “A hybrid color and frequency features method for face recognition”. Image Processing, IEEE Transactions, 2008, vol. 17, No. 10, pp. 1975-1980.
- A. H. Sahoolizadeh, B. Z. Heidari, and C. H. Dehghani, “A new face recognition method using pca, lda and neural network”. Proc. WASET, Julio 2008, vol. 31.
- H. Moon and P. J. Phillips, “Computational and performance aspects of pca-based face-recognition algorithms”. 2001, vol. 30, No. 3, pp. 303-321.
- K. Kim, “Face recognition using principal component analysis”.
- M. Turk and A. Pentland, “Eigenfaces for recognition”. J. Cognitive Neuroscience, January 1991, vol. 3, pp. 71-86.
- M. A. Turk and A. P. Pentland, “Face recognition using eigenfaces”. Proceedings IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1991, pp. 586-591.
- N. Morizet, F. Amiel, I. Hamed, and T. Ea, “A comparative implementation of pca face recognition algorithm”. in Electronics, Circuits and Systems, ICECS 2007. 14th IEEE International Conference, pp. 865-868.