Abstract
In this paper, multiple algorithm and score-level fusion for enhancing the performance of the face based biometric person authentication system is proposed. Though many algorithms are conferred, several crucial issues are still involved in the face authentication. Most traditional algorithms are based on certain assumptions failing which the system will not give appropriate results. Due to the inherent variations in face with time and space, it is a big challenge to formulate a single algorithm based on the face biometric that works well under all variations. This paper addresses the problem of illumination and pose variations, by using three different algorithms for face recognition: Block Independent Component Analysis (B-ICA), Discrete Cosine Transform (DCT) and Kalman filter. The weighted average based score level fusion is performed to improve the results obtained by the system. An intensive analysis of the various algorithms has been performed and the results indicate an increase in accuracy of the proposed system.
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References
Abboud AJ, Sellahewa H, Jassim SA (2009) Quality based approach for adaptive face recognition. In: Proceedings of mobile multimedia/image processing, security, and applications, SPIE, vol 7351, 73510N
Ozayy N, Frederick YT, Wheeler W, Liu X (2009) Improving face recognition with a quality-based probabilistic framework. In: IEEE computer society conference on computer vision and pattern recognition workshops, (CVPR Biometrics Workshop 2009), Miami Beach, Florida, pp 134–141
Brown LG (1992) A survey of image registration techniques-ACM Comput Surv 24(4): 325–376
Kong SG, Heo J, Boughorbel F, Zheng Y, Abidi BR, Koschan A, Yi M, Abidi MA (2007) Multiscale fusion of visible and thermal IR images for illumination-invariant face recognition. Int J Comput Vis 71(2):215–233
Looney D, Danilo PM (2009) Multiscale image fusion using complex extensions of EMD, IEEE Trans Sig Process 57(4)
Blum RS, Liu Z (2006) Multi-sensor image fusion and its applications. CRC Taylor & Francis group, First edition
Goshtasby A (2005) Fusion of multi-exposure images, image and vision computing, 23:611–618. http://www.imagefusion.org
Papageorgiou C, Poggio T (2000) A trainable system for object detection. Int J Comput Vision 38(1):15–33
Viola P, Jones MJ (2004) Robust real-time face detection, Int Jof Comput Vis 57(2):137–154
Lienhart R, Maydt J (2002) An extended set of Haar-like features for rapid object detection. IEEE ICIP 2002 1:900–903
Bartlett MS, Movellan JR, Sejnowski TJ (2002) Face recognition by independent component analysis. IEEE Trans Neural Networks 13(6):1450–1464
Zhang L, Gao Q, Zhang D (2007) Block independent component analysis for face recognition. 14th international conference on image analysis and processing (ICIAP), Modena, Italy 0-7695-2877-5/07
Matos FM, Batista LV, Poel 1 (2008) Face recognition using DCT coefficients selection. In: Proc. ACM symposium on applied computing, (SAC ’08), Fortaleza, Ceara, Brazil, pp 1753–1757
Welch G, Bishop G An introduction to the kalman filter, Technical report, University of North Carolina at Chapel Hill
Eidenberger H (2006) Kalman filtering for robust identification of face images with varying expressions and lighting conditions. In: 18th international conference on pattern recognition (ICPR’06), pp 1073–1076
Rafael CG, Richard EW (2007) Digital image processing. 3rd edn Prentice Hall, Inc., (now known as Pearson education, Inc.), upper Saddle river, New Jersey 07458, USA
Abdel-Mottaleb M, Mahoor MH (2007) Algorithms for assessing the quality of facial images, application notes, IEEE computational intelligence magazine (CIM), 2(2):10–17
Bradski G, Kaehler (2008) A Learning OpenCV. First Edition O’Reilly Media, Inc., 1005 Gravenstein Highway North USA
Vaidehi V, Treesa TM, Babu NTN, Annis Fathima A, Balamurali P, Chandra G Multi-algorithmic face authentication system, lecture notes in engineering and computer science: proceedings of the international multiconference of engineers and computer scientists 2011, IMECS 2011, 16–18 March 2011
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Authors acknowledge Tata Consultancy Service (TCS), Bangalore, INDIA, for supporting this work.
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Vaidehi, V. et al. (2012). Decision Level Fusion Framework for Face Authentication System. In: Ao, S., Castillo, O., Huang, X. (eds) Intelligent Control and Innovative Computing. Lecture Notes in Electrical Engineering, vol 110. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-1695-1_32
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DOI: https://doi.org/10.1007/978-1-4614-1695-1_32
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