Abstract
This paper proposes facial fraud discrimination using facial feature detection and classification based on the AdaBoost and a neural network. The proposed method detects the face, the two eyes, and the mouth by the AdaBoost detector. To classify detection results as either normal or abnormal eyes and mouths, we use a neural network. Using these results, we calculate the fraction of face images that contain normal eyes and mouths. These fractions are used for facial fraud detection by setting a threshold based on the cumulative density function of the Binomial distribution. The FRR and FAR of eye discrimination of our algorithm are 0.0486 and 0.0152, respectively. The FRR and FAR of mouth discrimination of our algorithm are 0.0702 and 0.0299, respectively.
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References
Financial success for biometrics? Biometric Technology Today (2005)
Dong, W., Soh, Y.: Image-based fraud detection in automatic teller machine. International Journal of Computer Science and Network Security 6, 13–18 (2006)
Lin, D., Liu, M.: Face occlusion detection for automated teller machine surveillance. In: Chang, L.-W., Lie, W.-N. (eds.) PSIVT 2006. LNCS, vol. 4319, pp. 641–651. Springer, Heidelberg (2006)
Freund, Y., Schapire, R.: A short introduction to boosting. Journal of Japanese Society for Artificial Intelligence 14, 771–780 (1999)
Fröba, B., Ernst, A.: Face detection with the modified census transform. In: Proceedings of IEEE International Conference on Automatic Face and Gesture Recognition, pp. 91–96 (2004)
Jun, B., Kim, D.: Robust real-time face detection using face certainty map. In: Proceedings of 2nd International Conference on Biometrics, pp. 29–38 (2007)
Choi, I., Kim, D.: Eye correction using correlation information. In: Yagi, Y., Kang, S.B., Kweon, I.S., Zha, H. (eds.) ACCV 2007, Part I. LNCS, vol. 4843, pp. 698–707. Springer, Heidelberg (2007)
Hinton, G., Salakhutdinov, R.: Reducing the dimensionality of data with neural networks. Science 313, 504–507 (2006)
Je, H., Kim, S., Jun, B., Kim, D., Kim, H., Sung, J., Bang, S.: Asian Face Image Database PF01. Database, Intelligent Multimedia Lab, Dept. of CSE, POSTECH (2001)
Lee, H., Park, S., Kang, B., Shin, J., Lee, J., Je, H., Jun, B., Kim, D.: The postech face database (pf07) and performance evaluation. In: Proceeding of 8th IEEE International Conference on Automatic Face and Gesture Recognition, pp. 1–6 (2009)
Martinez, A., Benavente, R.: The AR Face Database. CVC Technical Report #24 (1998)
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Choi, I., Kim, D. (2010). Facial Fraud Discrimination Using Detection and Classification. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2010. Lecture Notes in Computer Science, vol 6455. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17277-9_21
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DOI: https://doi.org/10.1007/978-3-642-17277-9_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17276-2
Online ISBN: 978-3-642-17277-9
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