Delina Beh Mei Yin
ABSTRACT
The paper presents a novel model of hybrid biometric-based authentication. Currently, the recognition accuracy of a single biometric verification system is often much reduced due to many factors such as the environment, user mode and physiological defects of an individual. Apparently, the enrolment of static biometric is highly vulnerable to impersonation attack. Due to the fact of single biometric authentication only offers one factor of verification, we proposed to hybrid two biometric attributes that consist of physiological and behavioural trait. In this study, we utilise the static and dynamic features of a human face. In order to extract the important features from a face, the primary steps taken are image pre-processing and face detection. Apparently, to distinguish between a genuine user or an imposter, the first authentication is to verify the user's identity through face recognition. Solely depending on a single modal biometric is possible to lead to false acceptance when two or more similar face features may result in a relatively high match score. However, it is found the False Acceptance Rate is 0.55% whereas the False Rejection Rate is 7%. By reason of the security discrepancies in the mentioned condition, therefore we proposed a fusion method whereby a genuine user will select a facial expression from the seven universal expression (i.e. happy, sad, anger, disgust, surprise, fear and neutral) as enrolled earlier in the database. For the proof of concept, it is proven in our results that even there are two or more users coincidently have the same face features, the selected facial expression will act as a password to be prominently distinguished a genuine or impostor user.
- M. Kyrnin. 2016. Biometrics will not replace the password, Tech for Anyone, 1 September, 2016.Google Scholar
- C. L. Deepika, and A. Kandaswamy. 2009. An algorithm for improved accuracy in unimodal biometric systems through fusion of multiple feature sets, ICGST-GVIP Journal, vol. 9, no. 3, pp. 33--40.Google Scholar
- A. Ross. 2004. Multimodal biometrics: An overview. In IEEE 12th European Signal Processing Conference, pp. 1221--1224.Google Scholar
- A. Hadid. 2014. Face biometrics under spoofing attacks: Vulnerabilities, countermeasures, open issues, and research directions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops pp. 113--118. Google ScholarDigital Library
- D. B. Mei Yin, M. I. Kamal, N. S. Azmanuddin, S. H. S. Ali, A. T. Othman, and R. Z. W. Chik, Electronic door access control using MyAccess two-factor authentication scheme featuring near-field communication and eigenface-based face recognition using principal component analysis. In Proceedings of the 10th International Conference on Ubiquitous Information Management and Communication, pp.1--8. Google ScholarDigital Library
- J. Galbally, J. Fierrez, J. Ortega-Garcia, C. McCool, and S. Marcel. 2009. Hill-climbing attack to an Eigenface-based face verification system. In 2009 First IEEE International Conference on Biometrics, Identity and Security, pp. 1--6.Google Scholar
- H. Ma, O. P. Popoola, and S. Sun. 2015. Research of dual- modal decision level fusion for fingerprint and finger vein image. International Journal of Biometrics, vol. 7, no. 3, pp. 271--285. Google ScholarDigital Library
- J. Yang, and X. Zhang. 2012. Feature-level fusion of fingerprint and finger-vein for personal identification. Pattern Recognition Letters, vol. 33, no. 5, pp. 623--628. Google ScholarDigital Library
- J. Peng, A. A. A. El-Latif, Q. Li, and X. Niu. 2014. Multimodal biometric authentication based on score level fusion of finger biometrics. Optik-International Journal for Light and Electron Optics, vol. 125, no. 23, pp. 6891--6897.Google ScholarCross Ref
- Y. Wang, T. Tan, and A. K. Jain. 2003. Combining face and iris biometrics for identity verification. In International Conference on Audio-and Video-Based Biometric Person Authentication, pp. 805--813. Google ScholarDigital Library
- G. E. Determan, V. C. Jacobson, J. Jelinek, T. Phinney, R. M. Hamza, T. Ahrens, G. A. Kilgore, and R. P. Whillock. 2016. Combined face and iris recognition system. U.S. Patent No. 8,705,808. Washington, DC: U.S. Patent and Trademark Office.Google Scholar
- J. Neves, F. Narducci, S. Barra, and H. Proença. 2016. Biometric recognition in surveillance scenarios: a survey," Artificial Intelligence Review, pp. 1--27. Google ScholarDigital Library
- A. A. M. Al-modwahi, O. Sebetela, L. N. Batleng, B. Parhizkar, and A. H. Lashkari. 2012. Facial expression recognition intelligent security system for real time surveillance. In Proceedings of the International Conference on Computer Graphics and Virtual Reality (CGVR) (p. 1). The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied ComputingGoogle Scholar
- Y. L. Tian, and R. M. Bolle. 2003. Automatic detecting neutral face for face authentication and facial expression analysis.," AAAI-03 Spring Symposium on Intelligent Multimedia Knowledge Management, vol. 3, pp. 24--26.Google Scholar
- C. Busso, Z. Deng, S. Yildirim, M. Bulut, C. M. Lee, A. Kazemzadeh, and S. Narayanan. 2004. Analysis of emotion recognition using facial expressions, speech and multimodal information. In Proceedings of the 6th international conference on Multimodal interfaces, pp. 205--211. Google ScholarDigital Library
- P. I. Wilson, and J. Fernandez. 2006. Facial feature detection using Haar classifiers. Journal of Computing Sciences in Colleges, vol. 21, no. 4, pp. 127--133. Google ScholarDigital Library
- P. Viola, and M. J. Jones. 2004. Robust real-time face detection. International Journal of Computer Vision, vol. 57, no. 2, pp. 137--154. Google ScholarDigital Library
- P. Viola, and M. Jones. 2001. Rapid object detection using boosted cascade of simple features. In Computer Vision and Pattern Recognition, Proceedings of the 2001 IEEE Computer Society Conference. pp. 1--511.Google Scholar
- R. Lienhart, and J. Maydt. 2002. An extended set of Haar-like features for rapid object detection. In Proceedings International Conference on Image Processing pp. 900--903.Google Scholar
- M. Turk, and A. Pentland. 1991. Eigenfaces for Face Detection / Recognition, Recognition, E., & Neuroscience, C., vol. 3, no. 1, pp. 1--11.Google Scholar
- Y. V. Lata, C. K. B. Tungathurthi, H. R. M. Rao, A. Govardhan, and L. P. Reddy. 2009. Facial Recognition using Eigenfaces by PCA. International Journal of Recent Trends in Engineering, vol. 1, no. 1.Google Scholar
- L. Sirovich, and M. Kirby. 1987. Low-dimensional procedure for the characterization of human faces.," Journal of the Optical Society of America, vol. 4, pp. 519--524.Google ScholarCross Ref
- V. Hiremath, and A. Mayakar. 2009. Face recognition using Eigenface approach. In IDT workshop on interesting results in computer science and engineering.Google Scholar
- Y. I. Tian, T. Kanade, and J. F. Cohn. 2001. Recognizing action units for facial expression analysis. In IEEE Transactions on pattern analysis and machine intelligence, vol. 23, no. 2, pp. 97--115. Google ScholarDigital Library
- P. Tsai, T. Hintz, and T. Jan. 2007. Facial behavior as behavior biometric? An empirical study. In 2007 IEEE International Conference on Systems, Man and Cybernetics. pp. 3917--3922.Google Scholar
- A. S. Dhavalikar, and R. K. Kulkarni. 2014. Face detection and facial expression recognition system. In Electronics and Communication Systems (ICECS), pp. 1--7.Google Scholar
- M. A. Butalia, M. Ingle, and P. Kulkarni. 2012. Facial expression recognition for security. International Journal of Modern Engineering Research (IJMER), vol. 2, pp. 1449--1453.Google Scholar
- B. Kaliyaperumal, and M. Rajasekaran. 2014. Application Authentication: Facial Expression Password.," International Conference Image Processing, Computers and Industrial Engineering., pp. 135--138.Google Scholar
- Y. Tie, and L. Guan, Automatic landmark point detection and tracking for human facial expressions, EURASIP Journal on Image and Video Processing, vol. 1, no. 1, pp. 1--15, 2013.Google Scholar
- Z. Zhang, P. Luo, C. C. Loy, and X. Tang. 2014. Facial landmark detection by deep multi-task learning. In European Conference on Computer Vision Springer International Publishing, pp. 94--108.Google Scholar
- E. Zhou, H. Fan, Z. Cao, Y. Jiang, and Q. Yin. 2013. Extensive facial landmark localization with coarse-to-fine convolutional network cascade, pp. 386--391. Google ScholarDigital Library
- C. Sagonas, E. Antonakos, G. Tzimiropoulos, S. Zafeiriou, and M. Pantic. 2016. 300 faces in-the-wild challenge: Database and results. In Image and Vision Computing, vol. 47, pp. 3--18. Google ScholarDigital Library
- P. Ekman, and W. V. Friesen. 1977. Facial actio'n coding system. Oxford University Press, USA.Google Scholar
- M. Pantic, and L. J. Rothkrantz. 1999. An expert system for multiple emotional classification of facial expressions. In Proceedings 11th IEEE International Conference on Tools with Artificial Intelligence, pp. 113--120. Google ScholarDigital Library
- T. Ane, and M. F. K. Patwary. 2016. Performance Analysis of Similarity Coefficient Feature Vector on Facial Expression Recognition. In Procedia Engineering, vol. 144, pp. 444--451.Google ScholarCross Ref
- F. Y. Shih, C.-F. Chuang, and P. S. Wang. 2008. Performance comparisons of facial expression recognition in JAFFE database. International Journal of Pattern Recognition and Artificial Intelligence, vol. 22, no. 03, pp. 445--459.Google ScholarCross Ref
- A. Jain, A. A. Ross, and K. Nandakumar, Introduction to biometrics: Springer Science & Business Media, 2011. Google ScholarDigital Library
Index Terms
- Fusion of face recognition and facial expression detection for authentication: a proposed model
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