Abstract
In the well explored domain of facial biometrics, methods which forgo traditional models of intruder detection can lead to a more robust system. One such method, which is outlined in this paper, is to utilize the movement of feature points over the course of an expression to make user authentication systems more secure. To do this, we developed a new algorithm, and used the process known as ranking, to describe facial expressions in a computationally cheap way. In our experiments, we performed 309,210 authentication attempts on 33 user profiles and achieved at best an error of only 3.4 %, with an average error of 33.28 %. Although such a system is not as accurate as common face biometric systems, we believe that this method can augment those systems to make them impervious to common attacks.
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References
Kose, N., Dugelay, J.L.: Shape and texture based countermeasure to protect face recognition systems against mask attacks. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 111–116. IEEE (2013)
Kose, N., Dugelay, J.L.: Countermeasure for the protection of face recognition systems against mask attacks. In: 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), pp. 1–6. IEEE (2013)
Kim, G., Eum, S., Suhr, J.K., Kim, D.I., Park, K.R., Kim, J.: InBiometrics (ICB), 2012 5th IAPR International Conference on Face Liveness Detection Based on Texture and Frequency Analyses, pp. 67–72. IEEE (2012)
Määttä, J., Hadid, A., Pietikäinen, M.: Face spoofing detection from single images usingtexture and local shape analysis. IET Biometrics 1(1), 3–10 (2012)
Frischholz, R.W., Werner, A.: Avoiding replay-attacks in a face recognition system using Head-pose Estimation. In: AMFG 2003. IEEE International Workshop on Analysis and Modeling of Faces and Gestures, pp. 234–235. IEEE (2003)
Lucey, P., Cohn, J.F., Kanade, T., Saragih, J., Ambadar, Z., Matthews, I.: The extended Cohn-Kanade Dataset (CK +): a complete dataset for action unit and emotion-specified expression. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 94–101. IEEE (2010)
Hong, L., Wan, Y., Jain, A.: Fingerprint image enhancement: algorithm and performance evaluation. IEEE Trans. Pattern Anal. Mach. Intell. 20(8), 777–789 (1998)
Reynolds, D.A., Rose, R.C.: Robust text-independent speaker identification using Gaussian mixture speaker models. IEEE Trans. Speech Audio Process. 3(1), 72–83 (1995)
Uludag, U., Jain, A.K.: Attacks on biometric systems: a case study in fingerprints. In: Electronic Imaging 2004, pp. 622–633. International Society for Optics and Photonics (2004)
Piotrowski, Z., Gajewski, P.: Voice spoofing as an impersonation attack and the way of protection. J. Inf. Assur. Secur. 2(3), 223–225 (2007)
Revenkar, P.S., Anjum, A., Gandhare, W.Z.: Secure iris authentication using visual (2010)
Tian, Y.L., Kanade, T., Cohn, J.F.: Recognizing action units for facial expression analysis. IEEE Trans. Pattern Anal. Mach. Intell. 23(2), 97–115 (2001)
Pantic, M., Rothkrantz, L.J.: Facial action recognition for facial expression analysis from static face images. Cybern. IEEE Trans. Syst. Man Cybern. Part B 34(3), 1449–1461 (2004)
Ioannou, S.V., Raouzaiou, A.T., Tzouvaras, V.A., Mailis, T.P., Karpouzis, K.C., Kollias, S.D.: Emotion recognition through facial expression analysis based on a neurofuzzy network. Neural Netw. 18(4), 423–435 (2005)
Shan, C., Gong, S., McOwan, P.W.: Facial expression recognition based on local binary patterns: a comprehensive study. Image Vis. Comput. 27(6), 803–816 (2009)
Noldus FaceReader 5. http://www.noldus.com/
Lee, D.T., Schachter, B.J.: Two algorithms for constructing a Delaunay triangulation. Int. J. Comput. Inf. Sci. 9(3), 219–242 (1980)
Bergadano, F., Gunetti, D., Picardi, C.: User authentication through keystroke dynamics. ACM Trans. Inf. Syst. Secur. (TISSEC) 5(4), 367–397 (2002)
Acknowledgements
This research was supported by the Michigan Space Grant Consortium. We thank our colleagues in the College of Science, Engineering, and Technology at Saginaw Valley State University for their continued support. In addition, we thank the Undergraduate Research Program (UGRP) at Saginaw Valley State University for making our continued works possible. In addition, we thank the students at Saginaw Valley State University for their valuable data and Cody Brown, whose assistance was invaluable.
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Tubbs, D.J., Rahman, K.A. (2015). Facial Expression Analysis as a Means for Additional Biometric Security in Recognition Systems. In: Dziech, A., Leszczuk, M., Baran, R. (eds) Multimedia Communications, Services and Security. MCSS 2015. Communications in Computer and Information Science, vol 566. Springer, Cham. https://doi.org/10.1007/978-3-319-26404-2_9
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DOI: https://doi.org/10.1007/978-3-319-26404-2_9
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