Abstract
Over the last decade or so, face recognition has become a popular area of research in computer vision and one of the most successful applications of image analysis and understanding. In addition, recognition of faces under varied poses has been a challenging area of research due to the complexity of pose dispersion in feature space. This paper presents a novel and robust pose-invariant face recognition method. In this approach, first, the facial region is detected using the TSL color model. The direction of face or pose is estimated using facial features and the estimated pose vector is decomposed into X-Y-Z axes. Second, the input face is mapped by a deformable template using these vectors and the 3D CANDIDE face model. Finally, the mapped face is transformed to the frontal face which appropriates for face recognition by the estimated pose vector. Through the experiments, we come to validate the application of face detection model and the method for estimating facial poses. Moreover, the tests show that recognition rate is greatly boosted through the normalization of the poses.
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References
Yongsheng, G.: Face recognition using line edge map. IEEE Trans. on Pattern Analysis and Machine Intelligence 24(6), 764–779 (2002)
Terrillon, J.C., Akamatsu, S.: Comparative performance of different chrominance spaces for color segmentation and detection of human faces in complex scene images. In: Proc. of the 4th IEEE Int’l. Conf. on Automatic Face and Gesture Recognition, pp. 54–60 (2000)
Yao, H., Gao, W.: Face locating and tracking method based on chroma transform in color images. In: Proc. Int’l. Conf. on Signal Processing, vol. 2, pp. 1367–1371 (2000)
Sung, K.K., Poggio, T.: Example based learning for view-based human face detection. IEEE Trans. on Pattern Recognition and Machine Intelligence 20, 39–51 (1998)
Ahlberg, J.: Model-based Coding: Extraction, Coding, and Evaluation of Face Model Parameters. Dissertations, Dept. of Electrical Engineering, Linköping University, Sweden (2002)
Brunelli, R., Poggio, T.: Face Recognition: Features versus Templates. IEEE Trans. on Pattern Analysis and Machine Intelligence 15(10), 1042–1052 (1993)
Beymer, D.J.: Pose-Invariant Face Recognition Using Real and Virtual Views. Ph.D. Thesis, Massachusetts Institute of Technology, Cambridge, MA (1995)
Turk, M., Pentland, A.: Eigenfaces for face recognition. Journal of Cognitive Neuroscience 3(1), 71–86 (1991)
Martinez, A.M., Kak, A.C.: PCA versus LDA. IEEE Trans. on Pattern Analysis and Machine Intelligence 23(2), 228–233 (2001)
Belhumeur, V.I., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. fisherfaces: Recognition using class specific linear projection. IEEE Trans. on Pattern Analysis and Machine Intelligence 19(7), 711–720 (1997)
Hwang, S.H., Choi, J.Y., Kim, N.B.: A Study on Face Reconstruction using Coefficients Estimation of Eigen-Face. In: Proc. of Korean Society for Internet Information, vol. 4(1), pp. 505–509 (2003)
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© 2005 Springer-Verlag Berlin Heidelberg
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Whangbo, TK., Choi, JY., Viswanathan, M., Kim, NB., Yang, YG. (2005). Pose-Invariant Face Recognition Using Deformation Analysis. In: De Gregorio, M., Di Maio, V., Frucci, M., Musio, C. (eds) Brain, Vision, and Artificial Intelligence. BVAI 2005. Lecture Notes in Computer Science, vol 3704. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11565123_53
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DOI: https://doi.org/10.1007/11565123_53
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29282-1
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