Abstract
Multimodal biometric has ability to improve the performance in biometric recognition system. In this paper we are using face biometrics to improve the performance of gait biometric. Here proposed multimodal biometrics use feature level fusion strategy. Here walking person face and gait data are captured in the form of image, and combination of this face and gait images PCA feature represent as combined feature vector. Principal component analysis algorithm is used to reduce the dimensionality of this PCA feature vector. At the testing phase we give the similar input vector which is the combination of both face and gait. Our experiments show that when we are using only gait feature of individual then its recognition rate is 67%. But when we combined gait with face then gait performance can be improved up to 90%.This system can be used in communities where automated method is require to determine the identity of individual.
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References
Ross, A., Jain, A.: Information fusion in biometrics. Pattern Recognition Letters (24), 2115–2125 (2003)
Yaoa, Y.-F., Jingb, X.-Y., Wong, H.-S.: Face and palmprint feature level fusion for single sample biometrics recognition. Neuro Computing 70(7-9) (March 2007)
Kala, R., Vazirani, H., Shukla, A., Tiwari, R.: Fusion of Speech and Face by Enhanced Modular Neural Network. In: Prasad, S.K., Vin, H.M., Sahni, S., Jaiswal, M.P., Thipakorn, B. (eds.) ICISTM 2010. CCIS, vol. 54, pp. 363–372. Springer, Heidelberg (2010)
Kumar, A., Kanhangad, V., Zhang, D.: A New frame work for adaptive multimodel biometrics management. IEEE Transactions of Information Forensics and Security 5(1) (March 2010)
Na, J.-H., Park, M.-S., Choi, J.-Y.: Pre-clustered principal component analysis for fast training of new face databases. In: Proc. Int. Conf. Control, Autom. Syst., pp. 1144–1149 (2007)
Zhao, G., Liu, J.: Application of principal component analysis and neural network on the information system evaluation. In: Pacific –Asia Conference on Circuits, Communications and System, pp. 785–788 (2009)
Turk, M., Pentland, A.: Eigenfaces for recognition. Journal of Cognitive Neuroscience 3, 71–86 (1991)
Kim, K.: Face Recognition using principal component analysis, USA (June 2000)
Ross, A., Jain, A.K.: Multimodal Biometrics: An Overview. In: Proceedings of the 12th European Signal Processing Conference, pp. 1221–1224 (2004)
Sanderson, C., Paliwal, K.K.: Identity verification using speech and face information. Digital Signal Processing 14(5), 449–480 (2004)
Yang, F., Ma, B., Wang, Q., Ya, D.: Information Fusion of Biometrics Based-on Fingerprint, Hand-geometry and Palm-print. In: 2007 IEEE Workshop on Automatic Identification Advanced Technologies, pp. 247–252 (2007)
Ahmad, M.I., Woo, W.L., Dlay, S.S.: Multimodal biometric fusion at feature level: Face and palmprint. In: 2010 7th International Symposium on Communication Systems Networks and Digital Signal Processing (CSNDSP), July 21-23, pp. 801–805 (2010)
CASIA Gait Database, http://www.cbsr.ia.ac.cn/english/Gait%20Databases.asp
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Sharma, S., Tiwari, R., Shukla, A., Singh, V. (2011). Fusion of Gait and Facial Feature Using PCA. In: Kim, Th., Adeli, H., Ramos, C., Kang, BH. (eds) Signal Processing, Image Processing and Pattern Recognition. SIP 2011. Communications in Computer and Information Science, vol 260. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27183-0_42
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DOI: https://doi.org/10.1007/978-3-642-27183-0_42
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