Abstract
Biometric face recognition presents a wide range of variability sources, such as make up, illumination, pose, facial expression, etc. Although some public available databases include these phenomena, it is a laboratory condition far away from real biometric system scenarios. In this paper we perform a set of experiments training and testing with different face databases in order to reduce the wide range of problems present in face images from different users (make up, facial expression, rotations, etc.). We use a novel dispersion matcher, which opposite to classical biometric systems, does not need to be trained with the whole set of users. It can recognize if two photos are of the same person, even if the photos of that person were not used in training the classifier.
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
Faundez Zanuy, M.: Biometric security technology. IEEE Aerospace and Electronic Systems Magazine 21(6), 15–26 (2006)
Faundez-Zanuy, M.: Biometric recognition: why not massively adopted yet? IEEE Aerospace and Electronic Systems Magazine 20(8), 25–28 (2005)
Faundez-Zanuy, M., Fierrez-Aguilar, J., Ortega-Garcia, J., Gonzalez-Rodriguez, J.: Multimodal biometric databases: an overview. IEEE Aerospace and electronic systems magazine 21(9), 29–37 (2006)
Nagy, G.: Candide’s practical principles of experimental pattern recognition. IEEE Trans. On Pattern Analysis and Machine Intelligence 5(2), 199–200 (1983)
Bolle, R.M., Ratha, N.K., Pankanti, S.: Performance evaluation in 1:1 Biometric engines. In: Li, S.Z., Lai, J.-H., Tan, T., Feng, G.-C., Wang, Y. (eds.) SINOBIOMETRICS 2004. LNCS, vol. 3338, pp. 27–46. Springer, Heidelberg (2004)
Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. In: Data Mining, Inference, and Prediction, Springer, Heidelberg (2001)
Bolle, R.M., Connell, J.H., Pankanti, S., Ratha, N.K., Senior, A.W.: Guide to Biometrics. Springer, Heidelberg (2004)
Rubinstein, Y.D., Hastie, T.: Discriminative vs Informative Learning, Knowledge Discovery and Data Mining, pp. 49–53 (1997)
Duda, R.O., Hart, P.E., Strork, D.G.: Pattern Classification, 2nd edn. Wiley-Interscience, Chichester (2001)
Samaria, F., Harter, A.: Parameterization of a stochastic model for human face identification. In: 2nd IEEE Workshop on Applications of Computer Vision, Sarasota (Florida) (December 1994)
Martinez, A.M.: Recognizing Imprecisely Localized, Partially Occluded, and Expression Variant Faces from a Single Sample per Class. IEEE Transaction On Pattern Analysis and Machine Intelligence 24(6), 748–763 (2002)
Lyons, M., Akamatsu, S., Kamachi, M., Gyoba, J.: Coding Facial Expressions with Gabor Wavelets. In: Third IEEE International Conference on Automatic Face and gesture
Jain, A.K., Bolle, R., Pankanti, S. (eds.): Biometrics, personal identification in networked society. Kluwer academic publishers, Dordrecht (1999)
Faundez-Zanuy, M., Roure-Alcobe, J., Espinosa-Duró, V., Ortega, J.A.: An efficient face verification method in a transformed domain. Pattern recognition letters 28(7), 854–858 (2007)
Faundez-Zanuy, M.: Data fusion in biometrics. IEEE Aerospace and Electronic Systems Magazine 20(1), 34–38 (2005)
Mansfield, A.J., Wayman, J.L.: Best Practices in Testing and Reporting Performance of Biometric Devices. Version 2.01. National Physical Laboratory Report CMSC 14/02 (August 2002)
Faundez-Zanuy, M.: Signature recognition state-of-the-art. IEEE Aerospace and Electronic Systems Magazine 20(7), 28–32 (2005)
Faundez-Zanuy, M., Fabregas, J.: On the relevance of facial expressions for biometric recognition. In: Esposito, A., et al. (eds.) Nonverbal Features of Human-Human and Human-Machine Interaction. LNCS. Springer, Heidelberg (submitted to, 2008) (to be published, 2008)
Turk, M., Pentland, A.: Eigenfaces for recognition. Int. J. Cog. Neurosci. 3(1), 71–86 (1991)
Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. Fisherface: Recognition using class specific linear projection. IEEE Trans. Pattern Anal. Machine Intell. 19, 711–720 (1997)
Yu, H., Yang, J.: A direct LDA algorithm for high-dimensional data with applications to face recognition. Pattern Recognit 34(12), 2067–2070 (2001)
Chien, J.T., Wu, C.C.: Discriminant waveletfaces and nearest feature classifiers for face recognition. IEEE Trans. Pattern Anal. Machine Intell. 24, 1644–1649 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Fabregas, J., Faundez-Zanuy, M. (2008). Biometric Face Recognition with Different Training and Testing Databases. In: Esposito, A., Bourbakis, N.G., Avouris, N., Hatzilygeroudis, I. (eds) Verbal and Nonverbal Features of Human-Human and Human-Machine Interaction. Lecture Notes in Computer Science(), vol 5042. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70872-8_4
Download citation
DOI: https://doi.org/10.1007/978-3-540-70872-8_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-70871-1
Online ISBN: 978-3-540-70872-8
eBook Packages: Computer ScienceComputer Science (R0)