Abstract
Multimodal biometrics recognition system suffers from the shortcomings of large data processing and much time cost during the recognition. To overcome the shortcomings of the traditional methods, in this paper, a novel multimodal biometrics recognition method is proposed by using image latent semantic analysis and extreme learning machine method. The image latent semantic analysis for multimodal biometrics feature extraction will extract abandon information from the images and the extreme learning machine method has the merits of high accuracy and fast speed. With this new method, the latent semantic features from the multimodal biometrics images are digged out to improve the recognition accuracy. Finally, the extreme learning machine is used as the classifier. The experiments show that the proposed algorithm has get better performances both in recognition accuracy and speed.
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References
Rattani, A., Kisku, D.R., Bicego, M., et al.: Feature Level Fusion Face and Fingerprint biometrics. In: First IEEE International Conference on Digital Object Identifier, pp. 1–6 (2007)
Conti, V., Militello, C., Sorbello, F.: A frequency-based approach for feature fusion in fingerprint and iris multimodal biometric identification system. IEEE Transactions on Systems, Man, and Cybernetics-Part C: Applications and Reviews 40(4), 384–395 (2010)
Fernandez, F.A., Fierrez, J., Ramos, D., Rodriguez, J.G.: Quality-Based Conditional processing in Multi-biometrics: Application to Sensor Interoperability. IEEE Transaction on Systems, Man and Cybernetics, Part A: Systems and Humans 40(6), 1168–1179 (2010)
Ross, A., Jain, A.: Information fusion in biometric. Pattern Recognition Letters 24(13), 2115–2125 (2003)
Yang, F., Ma, B.F.: A new mixed-mode biometrics information fusion based-on fingerprint, hand-geometry and palm-print. In: Fourth International Conference on Image and Graphics, pp. 689–693 (2007)
Fierrez-Aguilar, J., Ortega-Garcia, J., Gonzalea-Rodriguez, J.: Fusion strategies in Biometric multimodal verification. In: International Conference on Multimedia and Expo., vol. 3(III), pp. 5–8 (2003)
Ross, A.A., Govindarajan, R.: Feature level fusion using hand and face biometrics, pp. 196–204. International Society for Optics and Photonics (2005)
Singh, R., Vatsa, M., Noore, A.: Integrated multilevel image fusion and match score fusion of visible and infrared face images for robust face recognition. Pattern Recognition 41(3), 880–893 (2008)
Zhou, X.L., Bhanu, B.: Feature fusion of face and Gait for human recognition at a distance in video. In: International Conference on Pattern Recognition, pp. 529–532 (2006)
Deerwester, S., Dumais, S.T., Furnas, G.W., et al.: Indexing by latent semantic analysis. Journal of the American Society for Information Science 41(6), 391–407 (1990)
Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme Learning machine: Theory and Applications. Neurocomputing 70(1-3), 489–501 (2006)
Yang, J.C., Jiao, Y.B., Xiong, N., Park, D.S.: Fast Face Gender Recognition by Using Local Ternary Pattern and Extreme Learning Machine. KSII Transactions on Internet and Information Systems 7(7) (2013)
Pham, T.T., Maillot, N.E., Lim, J.H., et al.: Latent semantic fusion model for image retrieval and annotation. In: 16th ACM Conference on Conference on Information and knowledge Management, pp. 439–444 (2007)
Csurka, G.: Visual categorization with bags of key points Workshop on Statistical Learning in Computer Vision. In: ECCV (2004)
Fei-Fei, L., Fergus, R., Torralba, A.: Recognizing and learning object categories and A stochastic grammar of images
Yang, J.C., Park, D.S.: A fingerprint verification algorithm using tessellated invariant moment features. Neurocomputing 71(10), 1939–1946 (2008)
Yang, J., Zhang, D., Frang, A.F., et al.: Two-dimensional PCA: a new approach to appearance-based face representation and recognition, vol. 26(1), pp. 131–137 (2004)
Son, B., Lee, Y.: Biometric Authentication System Using Reduced Joint Feature Vector of Iris and Face. In: Kanade, T., Jain, A., Ratha, N.K. (eds.) AVBPA 2005. LNCS, vol. 3546, pp. 513–522. Springer, Heidelberg (2005)
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Yang, J., Jiao, Y., Wang, C., Wu, C., Chen, Y. (2013). Multimodal Biometrics Recognition Based on Image Latent Semantic Analysis and Extreme Learning Machine. In: Sun, Z., Shan, S., Yang, G., Zhou, J., Wang, Y., Yin, Y. (eds) Biometric Recognition. CCBR 2013. Lecture Notes in Computer Science, vol 8232. Springer, Cham. https://doi.org/10.1007/978-3-319-02961-0_54
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DOI: https://doi.org/10.1007/978-3-319-02961-0_54
Publisher Name: Springer, Cham
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