Abstract
Face recognition is becoming increasingly important in the contexts of computer vision, neuroscience, psychology, surveillance, credit card fraud detection, pattern recognition, neural network, content based video processing, assistive devices for visual impaired, etc. Face is a strong biometric trait for identification and hence criminals always try to hide their face by different artificial means such as plastic surgery, disguise and dummy. The availability of a comprehensive face database is crucial to test the performance of these face recognition algorithms. However, while existing publicly-available face databases contain face images with a wide variety of covariates such as poses, illumination, gestures and face occlusions but there is no dummy face database is available in public domain. The contributions of this paper are: i) Preparation of dummy face database of 50 subjects ii) Testing of face recognition algorithms on the dummy face database, iii) Critical analysis of four algorithms on dummy face database.
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
Introna, L.D., Nissenbaum, H.: Facial Recognition Technology. A Servey of Policy and Implementation Issues, CCPR
Zhao, W., Chellpa, R., Rosenfield, A., Phillips, P.J.: Face Recognition A Literature Survey
Bert, P.J., Adelson, E.H.: The Laplacian Pyramid as Compact Image Code. IEEE Transaction on Communication, COM-31(4) (April 1983)
Gonzalez, R.C., Woods, R.E.: Digital Image Processing. Pearson Education (2009)
Givens, G., Beveridge, J.R., Draper, B.A., Grother, P., Phillips, P.J.: How Features of the Human Face Affect Recognition: A Statistical Comparison of Three Face Recognition Algorithms. In: Proc. IEEE Int’l Conf. Computer Vision and Pattern Recognition, vol. 2 (2004)
Phillips, P.J., Flynn, P.J., Scruggs, T., Bowyer, K.W., Chang, J., Hoffman, K., Marques, J., Min, J., Worek, W.: Overview of the Face Recognition Grand Challenge. In: Proc. IEEE Int’l Conf. on Computer Vision and Pattern Recognition, pp. 947–954 (2005)
Phillips, P.J., Moon, H., Rizvi, S.A., Rauss, P.J.: The FERET Evaluation Methodology for Face Recognition Algorithms. IEEE Transaction on PAMI 22(10), 1090–1104 (2000)
Wang, P., Qiang, J., Wayman, J.L.: Modeling and Pridicting face recognition system Performance Based on analysis of similarity score. IEEE Transaction on PAMI 29 (2004)
Phillips, P.J., Moon, H., Rizvi, S.A., Rauss, P.J.: Face Evaluation Methodology for Face-Recognition Algorithms. Technical report NISTIR 6264 (January 1999)
Dong, H., Gu, N., Pohang: Asian Face Image Database PF01, Intelligent multimedia Lab. Technical Report, San 31, 790-784, Korea
Dai, G., Qian, Y.: Face Recognition Using Novel LDA-Based Algorithms
Jain, A.K., Hong, L., Pankanti, S.: Biometric Identification. Communication of the ACM 43(2) (February 2000)
Belhumeur, P., Hespanha, J., Kriegman, D.: Eigenfaces vs Fisherfaces: class specific linear projection. IEEE Transactions on PAMI 19(7), 711–720 (1997)
Martinez, A.M., Kak, A.C.: PCA versus LDA. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(2) (2001)
Turk, M., Pentland, A.: Eigenfaces for Recognition. J. Cognitive Neuroscience 3(1) (1991)
Samaria, F., Harter, A.: Parameterisation of a Stochastic Model for Human Face Identification. In: Proceedings of 2nd IEEE Workshop on Applications of Computer Vision, Sarasota FL (1994)
Sirvoich, L., Kirby, M.: A low dimensional Procedure for Characterization of Human Faces. J. Optical Soc. Am. A 4(3), 519–524 (1987)
Cardoso, J.F.: Infomax and Maximum Likelihood for Source Separation. IEEE Letters on Signal Processing 4, 112–114 (1997)
Hyvärinen, A., Karhunen, J., Oja, E.: Independent Component Analysis. John Wiley & Sons, New York (2001)
Hyvärinen, A.: The Fixed-point Algorithm and Maximum Likelihood Estimation for Independent Component Analysis. Neural Processing Letters 10, 1–5 (1999)
Liejun, W., Xizhong, Q., Taiyi, Z.: Facial Expression recognition using Support Vector Machine by modifying Kernels. Information Technology Journal 8, 595–599
Draper, B.A., Baek, K., Bartlett, M.S., Ross Beveridge, J.R.: Recognizing faces with PCA and ICA. Special issue on face recognition
Swets, D.L., Weng, J.J.: Using Discriminant Eigenfaces for Image Retrival. IEEE Transaction on PAMI 18(8), 831–836 (1996)
Yambor, W.S.: Analysis of Pca-Based and Fisher Discriminant-Based Image Recognition Algorithms. Technical Report CS-00-103 (July 2000)
Ahuja, M.S., Chhabra, S.: Effect of Distance Measures in Pca Based Face, Recognition. International Journal of Enterprise Computing and Business Systems 1(2) (2011) ISSN 2230-8849 (Online)
Agarwal, M., Jain, N., Kumar, M., Agrawal, H.: Face Recognition Using Eigen Faces and Artificial Neural Network. International Journal of Computer Theory and Engineering 2(4), 1793–8201 (2010)
Dagher, I.: Incremental PCA-LDA Algorithm. International Journal of Biometrics and Bioinformatics (IJBB) 4(2)
Draper, B.A., Baek, K., Bartlett, M.S., Ross Beveridge, J.: Recognizing Faces with PCA and ICA
Mazanec, J., Melisek, M., Oravec, M., Pavlovicov, J.: Support Vector Machines, Pca and Lda in Face Recognition. Journal of Electrical Engineering 59(4), 203–209 (2008)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag GmbH Berlin Heidelberg
About this paper
Cite this paper
Singh, A., Tiwari, S., Singh, S.K. (2012). Performance of Face Recognition Algorithms on Dummy Faces. In: Wyld, D., Zizka, J., Nagamalai, D. (eds) Advances in Computer Science, Engineering & Applications. Advances in Intelligent and Soft Computing, vol 166. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30157-5_22
Download citation
DOI: https://doi.org/10.1007/978-3-642-30157-5_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-30156-8
Online ISBN: 978-3-642-30157-5
eBook Packages: EngineeringEngineering (R0)