Abstract
The growing popularity of face biometrics for human authentication is due to its high user convenience and acceptance. Face recognition has evolved as an important application of biometrics and has been a topic of interest for several machine learning and computer vision communities. However, most of the attempts on face based personal authentication rely on decision threshold for accept or reject of the claimed identity. This paper investigates supervised learning techniques for face verification. The presented approach deals with computation of 5th level Haar wavelet coefficients of image used as feature for training of the classifiers like SVM, fuzzy SVM and KNN. The extracted biometric features are matched to compute genuine and impostor matching scores. The error rates FAR and FRR are then calculated using cross validation of the test set. The experiments are carried out on Yale database of 37 users with 25 images of each user. In our work we obtained FAR and FRR of 0.3285 and 0.1967 respectively which demonstrates the reliability of the proposed work.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Jain, A.K., Ross, A., Prabhakar, S.: An introduction to biometric recognition. IEEE Transactions on Circuits and Sytems for Radio Technonlogy 12(1) (January 2004)
Chellappa, R., Wilson, C.L., Sirohey, S.: Human and machine recognition of faces: A survey. Proc. IEEE 83, 705–740 (1995)
Wechsler, H., Phillips, P., Bruce, V., Soulie, F., Huang, T.: Face Recognition: From Theory to Applications. Springer (1996)
Bhuiyan, A., Liu, C.H.: On face recognition using Gabor filter. World Academy of Science, Engineering and Technology 28 (2007)
Barbu, T.: Gabor filter–based face recognition technique. In: Proceedings of the Romanian Academy series A 13(3), 277–283 (2010)
Mallat, S.G.: A wavelet tool of signal processing. Academy Press (1999)
Chang, C.-C., Chaung, J.-C., Hu, Y.-S.: Similar image retrievel based on wavelet transformation. International General of Wavelets, Multiresolution and Information Processing 2(2), 111–120 (2004)
Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, New York (1999)
Jieng, X., Yi, Z., Lv, J.C.: Fuzzy SVM with a new fuzzy membership function. Neural Compute and Applic. 15, 268–276 (2006)
Bioinformatics toolbox User’s Guide by the Math Works, Inc.
Daugman, J.G.: High confidence visual recognition of persons by a test of statistical independence. IEEE Transactions on Pattern Analysis and Machine Intelligence 15(13), 1348–1361 (1993)
Chang, C.-C., Lin, C.-J.: LIBSVM-A libary of Support vector machines. ACM Transactions on Intelligent Systems and Technology 2(3) (2011)
Yale face database, http://cvc.yale.edu/projects/yalefaces/yalefaces.html
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kumar, A., Gupta, R., Sharma, A., Panigrahi, B.K., Hanmandlu, M. (2012). Face Authentication Using Supervised Learning Techniques. In: Panigrahi, B.K., Das, S., Suganthan, P.N., Nanda, P.K. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2012. Lecture Notes in Computer Science, vol 7677. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35380-2_86
Download citation
DOI: https://doi.org/10.1007/978-3-642-35380-2_86
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35379-6
Online ISBN: 978-3-642-35380-2
eBook Packages: Computer ScienceComputer Science (R0)