Bilal Ahmed, Taner Çevik, Nazife ÇEVİK

8691

Data Fusion-Based Multimodal Biometric System for Face Recognition Using Manhattan Distance Penalty Weight

Data Fusion-Based Multimodal Biometric System for Face Recognition Using Manhattan Distance Penalty Weight

In this paper, we propose a multimodal biometric face recognition technique which is mainly based on the 2D Discrete Wavelet Transform (DWT) and Data Fusion (DF) and utilizes data fusion techniques at the score level of the system algorithm. The technique employs three discrete unimodal feature extraction and classification methods. The first two feature vectors are generated from raw images by using Principal Component Analysis (PCA) and Local Binary Pattern (LBP) methods. During the generation of the third feature vector, images are initially transformed into the DWT domain. In result, approximation, vertical, horizontal and diagonal detail matrices are combined to form a Joint Feature Vector (JFV). K-Nearest Neighbor (KNN) classifier algorithm is separately applied to the three generated feature vectors to compute different score values for the same individual. These raw score values are fused together using a newly proposed data fusion technique based on Manhattan Distance Penalty Weighting (MDPW). The proposed MDPW penalizes an individual for scoring low points and further pushes it away from the potentially winning class before data fusion is conducted. The proposed approach was implemented on ORL and YALE public face databases. The results of the proposed approach are evaluated using the recognition rates and receiver operating characteristics of the biometric classification systems. Experimental results show that the proposed multimodal system performs better than the unimodal system and other multimodal systems that use different data fusion rules (e.g. Sum Rule or Product Rule). In ORL database, the recognition rate of up to 97% can be achieved using the proposed technique.
Keywords:

Face recognition, discrete wavelet transform, principal component analysis, local binary pattern data fusion, Manhattan distance, k-nearest neighbor (KNN),

PDF

___

  • Belhumeur, P., et al., 1997. Eigenfaces versus fisherfaces: recognition using class specific linear projection, IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 711-720.
  • Beyreuther, M., 2011. Speech recognition based automatic earthquake detection and classification, Ludwig Maximilians University, Muenchen, Fakultaet fuer Geowissenschaften, Ph.D. thesis.
  • Bowyer, K.W., et al., 2006. Face recognition using 2-D, 3-D, and infrared: is multimodal better than multisample ?, Proceedings of the IEEE, vol. 94, no. 11, pp. 2000-2012.
  • Bromba, M.U.A., Bioidentification frequently asked questions, available at http://www.bromba.com/faq/biofaqe.htm#ROC.
  • Campbell, J., 1997. Speaker recognition: a tutorial, Proceedings of IEEE, 85, 9, pp. 1437-1462.
  • CASIA iris image database, 2006. http://www.sinobiometrics.com .
  • Chellappa, R., et al., 1995. Human and machine recognition of faces: a survey. Proceedings of the IEEE, 83,5, pp. 705-740.
  • Ross, A., et al., 2006. Handbook of Multibiometrics, SpringerScience + Business Media, LLC.
  • Ratha, N.K., et al., 1998. Image mosaicing for rolled fingerprint construction. Proc. Int. Conf. Pattern Recog., vol. 2, pp. 1651–1653.
  • Singh, S., et al., 2004. Infrared and visible image fusion for face recognition. SPIE Defense and Security Symposium, pp.585-596.
  • Heo, J. Kong, et al., 2004. Fusion of visual and thermal signatures with eyeglass removal for robust face recognition. Proc. Joint IEEE Workshop Object Tracking and Classification beyond the Visible Spectrum.
  • Son, B., and Lee, Y., 2005. Biometric authentication system using reduced Joint feature vector of iris and face. Lecture Notes in Computer Science, vol. 3546, pp. 261-273.
  • Ross A., and Govindarajan, R., 2005. Feature level fusion using hand and face biometrics. Proc. SPIE Conf. Biometric Technology for Human Identification II, pp. 196–204.
  • Kittler, J., et al., 1998. On combining classifiers. IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 20, no. 3, pp. 226–239.
  • Verlinde, P. and Cholet, G., 1999. Comparing decision fusion paradigms using k-NN based classifiers, decision trees and logistic regression in a multi-modal identity verification application. Proc. Int. Conf. Audio and Video-Based Biometric Person Authentication.
  • (AVBPA), 1999. Washington, DC, pp. 188–193.
  • Chen, X., et al., 2005. IR and visible light face recognition. Computer Vision and Image Understanding, vol. 99. no. 3, pp. 332–358.
  • Das, R., 2007. Signature Recognition. Keesing Journal of Documents & Identity, issue 24.
  • Dass, S.C., et al., 2005. A principled approach to score level fusion in multimodal biometric systems. In Fifth AVBPA, Rye Brook, pp. 1049–1058, July.
  • Daugman, J.G., 2004. How iris recognition works. IEEE Trans. Circuits Syst. Video Technol., vol. 14, no. 1, pp. 21–30.
  • Frischholz, R.W., et al., 1994. Face recognition with the synergetic computer. Proc. Int’l Conf. Applied Synergetics and Synergetic Eng., Fraunhofer Gesellschaft für Integrierte Schaltungen, Erlangen, Germany, pp. 107-11.
  • A. M. Ashir and A. Eleyan, 2015. A multi-resolution Approach for Edge Detection using Ant Colony Optimization. 23nd IEEE International Conference on Signal Processing and Communications Applications (SIU), Malatya, Turkey, pp. 1777 – 1780.
  • Hampel, F.R., et al., 1986. Robust Statistics: The Approach Based on Influence Functions. New York: Wiley.
  • Han, J., and Bhanu, B., 2005. “Gait recognition by combining classifiers based on environmental contexts”, Lecture Notes in Computer Science, vol. 3546/2005, pp. 113-124.
  • Ho, T.K., et al., 1994. Decision combination in multiple classifier systems. IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 16, no. 1, pp. 66-75.
  • Huber, P.J., 1981. Robust Statistics. New York: Wiley. 21. Human identification at a distance. http://www.equinoxsensors.com/products/HID.html.
  • Indovina, M., et al., 2003. Multimodal biometric authentication methods: A COTS approach. Proc. MMUA 2003, Workshop Multimodal User Authentication, pp. 99-106.
  • ISO/IEC 24745:2011, 2011. Information technology - Security techniques – biometric information protection.
  • ISO/IEC TR 24722:2007, 2007. Information technology– Biometrics: Multimodal and other multibiometric fusion.
  • Jain A., et al. 2005. Score normalization in multimodal biometric systems. Pattern Recognition, vol. 38, no.12, pp. 2270–228.
  • Jain, A., et al., 1999. A multimodal biometric system using fingerprint, face and speech. Second Internat. Conf. on AVBPA, Washington, DC, USA. pp. 182-187.
  • Jain, A., et al., 1997. On-line fingerprint verification. IEEE Trans. Pattern Anal. and Machine Intell., 19, 4, pp. 302-314.
  • Jain, A.K., and Ross, A., 2002. Fingerprint mosaicking. Proc. Int’l Conf Acoustic Speech and Signal Processing, vol. 4, pp. 4064-4067.
International Journal of Electronics Mechanical and Mechatronics Engineering-Cover
  • ISSN: 2146-0604
  • Başlangıç: 2011
  • Yayıncı: İstanbul Aydın Üniversitesi
Arşiv
Sayıdaki Diğer Makaleler

Implementation and Evaluation of Maximum Power Point Tracking (MPPT) Based on Adaptive Neuro-Fuzzy Inference System for Photovoltaic PV System

Abd El Hakim Ali ELAGORİ, M. Emin TACER

Data Fusion-Based Multimodal Biometric System for Face Recognition Using Manhattan Distance Penalty Weight

Bilal Ahmed, Taner Çevik, Nazife ÇEVİK

A New Compact Circular Polarized CPW Antenna Design for UWB Application

Hemrah HIVEHCHI, Saeid KARAMZADEH