Abstract
Multi-biometrics has recently emerged as a mean of more robust and efficient personal verification and identification. Exploiting information from multiple sources at various levels i.e., feature, score, rank or decision, the false acceptance and rejection rates can be considerably reduced. Among all, feature level fusion is relatively an understudied problem. This paper addresses the feature level fusion of multi-modal and multi-unit sources of information. For multi-modal fusion the face and iris biometric traits are considered, while the multi-unit fusion is applied to merge the data from the left and right iris images. The proposed approach computes the SIFT features from both biometric sources, either multi-modal or multi-unit. For each source, feature selection on the extracted SIFT features is performed via spatial sampling. Then these selected features are finally concatenated together into a single feature super-vector using serial fusion. This concatenated super feature vector is used to perform classification.
Experimental results from face and iris standard biometric databases are presented. The reported results clearly show the performance improvements in classification obtained by applying feature level fusion for both multi-modal and multi-unit biometrics in comparison to uni-modal classification and score level fusion.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Hong, L., Jain, A., Pankanti, S.: Can Multi-biometrics Improve performance. In: Proc. of AutoID 1999, pp. 59–64 (1999)
Jain, A.K., Ross, A.: Multi-biometric systems. Communications of the ACM 47(1), 34–40 (2004)
Ross, A., Jain, A.K.: Information Fusion in Biometrics. Pattern Recognition Letters 24, 2115–2125 (2003)
Chibelushi, C.C., Mason, J.S., Deravi, F.: Integration of acoustic and visual speech for speaker recognition. In: EUROSPEECH 1993, pp. 157–160 (1993)
Duc, B., Maître, G., Fischer, S., Bigün, J.: Person authentication by fusing face and speech information. In: Bigün, J., Borgefors, G., Chollet, G. (eds.) AVBPA 1997. LNCS, vol. 1206. Springer, Heidelberg (1997)
Hong, L., Jain, A.: Integrating Faces and Fingerprints for Personal Identification. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(12), 1295–1307 (1998)
Ross, A., Govindarajan, R.: Feature Level Fusion Using Hand and Face Biometrics. In: Proc. of SPIE Conference on Biometric Technology for Human Identification II, Orlando, USA, pp. 196–204 (2005)
Zhou, X., Bhanu, B.: Feature fusion of face and Gait for Human Recognition at a distance in video. In: International Conference on Pattern Recognition, Hong kong, (2006)
Singh, S., Gyaourova, G., Pavlidis, I.: Infrared and visible image fusion for face recognition. In: SPIE Defense and Security Symposium, pp. 585–596 (2004)
Wang, Y., Tan, T., Jain, A.K.: Combining Face and Iris Biometrics for Identity Verification. In: Kittler, J., Nixon, M.S. (eds.) AVBPA 2003. LNCS, vol. 2688, pp. 805–813. Springer, Heidelberg (2003)
Zhang, Z., Wang, R., Pan, K., Li, S.Z., Zhang, P.: Fusion of near infrared face and iris biometrics. In: Lee, S.-W., Li, S.Z. (eds.) ICB 2007. LNCS, vol. 4642, pp. 172–180. Springer, Heidelberg (2007)
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)
Gan, J., Liang, Y.: A Method for Face and Iris Feature Fusion in Identity Authentication. IJCSNS, 6 ( 2B) (2006)
Lowe, David, G.: Object recognition from local scale invariant features. In: International Conference on Computer Vision, Corfu, Greece, pp. 1150–1157 (September 1999)
Bicego, M., Lagorio, A., Grosso, E., Tistarelli, M.: On the use of SIFT features for face authentication. In: Proc. of Int. Workshop on Biometrics, in association with CVPR (2006)
Park, U., Pankanti, S., Jain, A.K.: Fingerprint Verification using SIFT Features. In: Proc. of SPIE Defense and Security Symposium, Orlando, Florida (2008)
Ross, A., Shah, S.: Segmenting Non-ideal Irises Using Geodesic Active Contours. In: Proc. of Biometrics Symposium (BSYM), Baltimore, USA (2006)
http://www.cbsr.ia.ac.cn/english/IrisDatabase.asp
http://www.equinoxsensors.com/products/HID.html
Wu, X., Wang, K., Zhang, D., Qi, N.: Combining left and right irises for personal authentication. In: Yuille, A.L., Zhu, S.-C., Cremers, D., Wang, Y. (eds.) EMMCVPR 2007. LNCS, vol. 4679, pp. 145–152. Springer, Heidelberg (2007)
Matey, J.R., Naroditsky, O., Hanna, K., Kolczynski, R., LoIacono, D.J., Mangru, S., Tinker, M., Zappia, T.M., Zhao, W.Y.: Iris on the Move: Acquisition of Images for Iris Recognition in Less Constrained Environments. Proc. of the IEEE 94(11), 1936–1947 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Rattani, A., Tistarelli, M. (2009). Robust Multi-modal and Multi-unit Feature Level Fusion of Face and Iris Biometrics. In: Tistarelli, M., Nixon, M.S. (eds) Advances in Biometrics. ICB 2009. Lecture Notes in Computer Science, vol 5558. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01793-3_97
Download citation
DOI: https://doi.org/10.1007/978-3-642-01793-3_97
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-01792-6
Online ISBN: 978-3-642-01793-3
eBook Packages: Computer ScienceComputer Science (R0)