Home   >   CSC-OpenAccess Library   >    Manuscript Information
Robust Analysis of Multibiometric Fusion Versus Ensemble Learning Schemes: A Case Study Using Face and Palmprint
Supreetha Gowda H. D., Hemantha Kumar G., Mohammad Imran
Pages - 24 - 33     |    Revised - 31-10-2016     |    Published - 01-12-2016
Volume - 10   Issue - 3    |    Publication Date - December 2016  Table of Contents
Fusion, Multi Biometrics, Ensemble, Support Vectors, Perceptron, Probability.
Identification of person using multiple biometric is very common approach used in existing user validation of systems. Most of multibiometric system depends on fusion schemes, as much of the fusion techniques have shown promising results in literature, due to the fact of combining multiple biometric modalities with suitable fusion schemes. However, similar type of practices are found in ensemble of classifiers, which increases the classification accuracy while combining different types of classifiers. In this paper, we have evaluated comparative study of traditional fusion methods like feature level and score level fusion with the well-known ensemble methods such as bagging and boosting. Precisely, for our frame work experimentations, we have fused face and palmprint modalities and we have employed probability model - Naive Bayes (NB), neural network model - Multi Layer Perceptron (MLP), supervised machine learning algorithm - Support Vector Machine (SVM) classifiers for our experimentation. Nevertheless, machine learning ensemble approaches namely, Boosting and Bagging are statistically well recognized. From experimental results, in biometric fusion the traditional method, score level fusion is highly recommended strategy than ensemble learning techniques.
1 Google Scholar 
2 CiteSeerX 
3 refSeek 
4 Scribd 
5 SlideShare 
6 PdfSR 
A. Ross and A.K. Jain, ?Information fusion in biometrics,? Pattern Recognition Letters, vol. 24, no. 13, pp. 2115-2125, 2003.
A. Ross and R.Govindarajan, ?Feature level fusion using hand and face biometrics,? in Proceedings of SPIE Conference on Biometric Technology for Human Identification, 2004, pp. 196-204.
A.Ross, K.Nandakumar, and A.K. Jain, Handbook of Multibiometrics, Springer-Verlag edition, 2006.
Brown and Gavin. Ensemble learning. In Encyclopedia of Machine Learning, pages 312- 320. Springer US, 2010. ISBN 978-0-387-30768-8.
Brown and Gavin. Ensemble learning. In Encyclopedia of Machine Learning,pages 312-320. Springer US, 2010. ISBN 978-0-387-30768-8, doi 10.1007/978-0-387-30164-8252.
D. Maturana, D. Mery and A Soto, "Face Recognition with Local Binary Patterns, Spatial Pyramid Histograms and Naive Bayes Nearest Neighbor Classification," Chilean Computer Science Society (SCCC), 2009 International Conference of the, Santiago, TBD, Chile, 2009, pp. 125-132.
G. Chakraborty, B. Chakraborty, J. C. Patra and C. Pornavalai, "An MLP-based face authentication technique robust to orientation," 2009 International Joint Conference on Neural Networks, Atlanta, GA, 2009, pp. 481-488.
H. Y. Chen, C. L. Huang and C. M. Fu, "Hybrid-Boost Learning for Multi-Pose Face Detection and Facial Expression Recognition," 2007 IEEE International Conference on Multimedia and Expo, Beijing, 2007, pp. 671-674.
Josef Kittler, Mohamad Hatef, Robert P. W. Duin, and Jiri Matas. On combining classi_ers. IEEE Trans. Pattern Anal. Mach. Intell., 20(3):226-239, March 1998.
L. Breiman, Bagging predictors, Machine Learning 24 (2) (1996) 123-140.
M. Choras, "Emerging Methods of Biometrics Human Identification," Innovative Computing, Information and Control, 2007. ICICIC '07. Second International Conference on, Kumamoto, 2007, pp. 365-365.
Mohammad Imran, Ashok Rao, Hemanthakumar G.: Extreme Subjectivity of Multimodal Biometrics Solutions: Role of Strong and Weak modalities / features information. IICAI 2011: 1587-160.
Nanni, Loris, et al. "Combining biometric matchers by means of machine learning and statistical approaches." Neurocomputing 149 (2015): 526-535.
P. Kocjan and K. Saeed, "A Feature Based Algorithm for Face Image Description," Biometrics and Kansei Engineering (ICBAKE), 2011 International Conference on, Takamatsu, Kagawa, 2011, pp. 175-178.
R.E. Schapire, The strength of weak learnability, Machine Learning 5 (2) (1990) 197-227.
Richard O. Duda, Petre E Hart, David G, and Strock. Pattern Classi_cation. John Wiley & Sons, 2000.
Rosenblatt and Frank. The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review, 65(6):386-408,1958.
S. Chaudhary and R. Nath, "A Multimodal Biometric Recognition System Based on Fusion of Palmprint, Fingerprint and Face," Advances in Recent Technologies in Communication and Computing, 2009. ARTCom '09. International Conference on, Kottayam, Kerala, 2009, pp. 596-600.
S. Y. Kung, Shang-Hung Lin and Ming Fang, "A neural network approach to face/palm recognition," Neural Networks for Signal Processing [1995] V. Proceedings of the 1995 IEEE Workshop, Cambridge, MA, 1995, pp. 323-332.
T. A. Budi Wirayuda, D. H. Kuswanto, H. A. Adhi and R. N. Dayawati, "Implementation of feature extraction based hand geometry in biometric identification system," Information and Communication Technology (ICoICT), 2013 International Conference of, Bandung, 2013, pp. 259-263.
Vladimir N. Vapnik. The nature of statistical learning theory. Springer-VerlagNew York, Inc., New York, NY, USA, 1995. ISBN: 0-387-94559-8.
Wang, Ruihu. "AdaBoost for feature selection, classification and its relation with SVM, a review." Physics Procedia 25 (2012): 800-807.
X.Y. Jing, Y.F. Yao, J.Y. Yang, M. Li, and D. Zhang, ?Face and palmprint pixel level fusion and kernel DCVRBF classifier for small sample biometric recognition,? Pattern Recognition, vol. 40, no. 3, pp. 3209-3224, 2007.
Y.Yao, X. Jing, and H. Wong, ?Face and palmprint feature level fusion for single sample biometric recognition?, Nerocomputing, vol. 70, no. 7-9, pp. 1582-1586, 2007.
Yoav Freund and Robert E. Schapire. Experiments with a new boosting algorithm. In Machine Learning: Proceedings of the Thirteenth International Conference, pages 148-156, 1996.
Miss Supreetha Gowda H. D.
Department of Computer Science University of Mysore Mysore - India
Mr. Hemantha Kumar G.
Department of Computer Science University of mysore Mysore - India
Dr. Mohammad Imran
Department of CCSIT, King Faisal University, Al-Ahsa - Saudi Arabia