Abstract
In recent years much advancement have been made in face recognition techniques to cater to the challenges such as pose, expression, illumination, aging and disguise. However, due to advances in technology, there are new emerging challenges for which the performance of face recognition systems degrades and plastic/cosmetic surgery is one of them. In this paper we comment on the effect of plastic surgery face image in multimodal biometric face recognition using speech signal. Speaker identity is correlated with the physiological and behavioral characteristics of the speaker. Selecting the most effective fusion techniques depends on operational issues such as accuracy requirements, availability of training data, and the validity of simplifying assumptions.
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Kong, S., Heo, J., Abidi, B., Paik, J., Abidi, M.: Recent advances in visual and infrared face recognition - A review. Computer Vision and Image Understanding 97(1), 103-135, 1077–3142 (2005)
Kriegman, D., Yang, M., Ahuja, N.: Detecting faces in images: a survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(1), 34-58, 0162–8828 (2002)
Liu, X., Chen, T., Kumar, V.: On modeling variations for face authentication. In: Proceedings of International Conference Automatic Face and Gesture Recognition, pp. 369–374 ( May 2002)
Gu, Y., Thomas, T.: A hybrid score measurement for HMM-based speaker verification. In: Proceedings of IEEE International Conference Acoustics, Speech, and Signal Processing, vol. 1, pp. 317–320 (1999)
Rowley, H., Baluja, S., Kanade, T.: Neural network-based face detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(1), 203-208, 0162–8828 (1998)
Osuna, E., Freund, R., Girosi, F.: Training support vector machines: an application to face detection. In: Proceeding of IEEE Conference Computer Vision and Pattern Recognition, pp. 130–136 (1997)
Samaria, F., Young, S.: HMM based architecture for face identification. Image and Vision Computing 12(8), 537-543, 262–8856 (1994)
Belhumeur, P., Hespanha, J., Kriegman, D.: Eigenfaces vs fisherfaces: recognition using class specification linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(7), 711-720, 162–8828 (1997)
Li, S., Chu, R., Liao, S., Zhang, L.: Illumination invariant face recognition using near-infrared images. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(4), 627–639 (2007)
Singh, R., Vatsa, M., Noore, A.: Improving verification accuracy by synthesis of locally enhanced biometric images and deformable model. Signal Processing 87(11), 2746–2764 (2007)
Blanz, V., Romdhami, S., Vetter, T.: Face identification across different poses and illuminations with a 3d morphable model. In: Proceedings of International Conference on Automatic Face and Gesture Recognition, pp. 202–207 (2002)
Liu, X., Chen, T.: Pose-robust face recognition using geometry assisted probabilistic modeling. In: Proceedings of International Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 502–509 (2005)
Singh, R., Vatsa, M., Ross, A., Noore, A.: A mosaicing scheme for pose-invariant face recognition. IEEE Transactions on Systems, Man and Cybernetics - Part B 37(5), 1212–1225 (2007)
Lanitis, A., Taylor, C.J., Cootes, T.F.: Toward automatic simulation of aging effects on face images. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(4), 442–450 (2002)
Ramanathan, N., Chellappa, R.: Face verification across age progression. IEEE Transactions on Image Processing 15(11), 3349–3362 (2006)
Ramanathan, N., Chowdhury, A.R., Chellappa, R.: Facial similarity across age, disguise, illumination and pose. In: Proceedings of International Conference on Image Processing, pp. 1999–2002 (2004)
Singh, R., Vatsa, M., Noore, A.: Face recognition with disguise and single gallery images. Image and Vision Computing 27(3), 245–257 (2009)
Brunelli, R., Falavigna, D.: Person Identification Using Multiple Cues. IEEE Trans. PAMI 17(10), 955–966 (1995)
Kittler, J., Hatef, M., Duin, R.P.W., Matas, J.: On Combining Classifiers. IEEE Trans. PAMI 20(3), 226–239 (1998)
Hong, L., Jain, A.K.: Integrating Faces and Fingerprints for Personal Identificaion. IEEE Trans. PAMI 20(12), 1295–1307 (1998)
Ben-Yacoub, S., Abdeljaoued, Y., Mayoraz, E.: Fusion of Face and Speech Data for Person Identity Verification. IEEE Trans. N Networks 10(5) (1999)
Ross, A., Jain, A.K.: Information Fusion in Biometrics. Pattern Recognition Letters 24(13), 2115–2125 (2003)
Kittler, J., Hatef, M., Duin, R., Matas, J.: On Combining Classifiers. IEEE Trans on Pattern Analysis and Machine Intelligence 20(3) (March 1998)
Ben-Yacoub, S., Abdeljaoued, S., Mayoraz, E.: Fusion of Face and Speech Data for Person Identity Verification (1999)
Fierrez-Aguilar, J., Ortega-Garcia, J., Garcia-Romero, D., Gonzalez-Rodriguez, J.: A comparative evaluation of fusion strategies for multimodal biometric verification. In: Kittler, J., Nixon, M.S. (eds.) AVBPA 2003. LNCS, vol. 2688, pp. 830–837. Springer, Heidelberg (2003)
Chen, S.C., Zhu, Y.L., Zhang, D.Q., Yang, J.Y.: Feature extraction approaches baseed on matrix pattern: MatPCA & MatFLDA. Pattern Recognition Letters 26(8) (2005)
Turk, M., Pentland, A.: Eigenfaces for recog. J. Cognitive Neuroscience (1991)
Yang, J., Zhang, D., Frangi, A.F., Yang, J.Y.: Two-dimensional PCA: a new approach to appearance-based face representation and recognition. IEEE Trans. on Pattern Analysis and Machine Intelligence 26(1), 131–137 (2004)
Zhang, D.Q., Chen, S.C., Liu, J.: Representing image matrices: Eigenimages vs. Eigenvectors. In: Wang, J., Liao, X.-F., Yi, Z. (eds.) ISNN 2005. LNCS, vol. 3497, pp. 659–664. Springer, Heidelberg (2005)
Yee, C.S., Ahmad, A.M.: Malay Language Text Independent Speaker Vertification using NN-MLP classsifier with MFCC (2008)
Lockwood, P., Boudy, J.: Experiments with a Nonlinear Spectral Subtractor (NSS), Hidden Markov Models and the Projection, for Robust Speech Reco (1992)
Rosenberg, A., Lee, C.-H., Soong, F.: Cepstral Channel Normalization Techniques for HMM Based Speaker Verification (1994)
Jackson, P.: Features extraction 1.ppt, University of Surrey, GU2 & 7XH
Kittler, J., Hatef, M., Duin, R.P., Matas, J.G.: On combining classifiers. IEEE Trans. Pattern Anal. Mach. Intell. 20(3), 226–239 (1998)
Verlinde, P., Druyts, P., Cholet, G., Acheroy, M.: Applying Bayes based classifiers for decision fusion in a multimodal identity verification system. In: Proceedings of International Symposium on Pattern Recognition In Memoriam Pierre Devijver, Brussels, Belgium (1999)
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Nageshkumar, M., ShanmukhaSwamy, M.N. (2011). An Iterative Method for Multimodal Biometric Face Recognition Using Speech Signal. In: Meghanathan, N., Kaushik, B.K., Nagamalai, D. (eds) Advances in Computer Science and Information Technology. CCSIT 2011. Communications in Computer and Information Science, vol 131. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17857-3_30
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DOI: https://doi.org/10.1007/978-3-642-17857-3_30
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