Abstract
Biometric face reorganization is an established means for the prevention of frauds in financial transactions and security issues. In particular, face verification has been extensively used to endorse financial transactions. Thermal face recognition is an upcoming approach in this field. This work proposes a robust thermal face recognition system based on face localized scale-invariant feature transform (FLSIFT). FLSIFT tackles the problem of thermal face recognition with complex backgrounds. Experimental results of proposed FLSIFT thermal face recognition system are compared with the existing Blood Vessel Pattern method. To test the performance of proposed and existing method, a new thermal face database consisting of Indian people and variations in the background is developed. The thermal facial images of 113 subjects are captured for this purpose. The test results show that the recognition accuracy of Blood Vessel Pattern technique and FLSIFT on face images with simple background is 79.28 % and 100 %, respectively. Moreover, the test performance on the complex background for the two methods is found to be 5.55 % and 98.14 %, respectively. It may be noted that FLSIFT is capable to handle background changes more efficiently and the performance is found to be robust.
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References
Chen, X., Flynn, P.J. & Bowyer, K. W. PCA-based face recognition in infrared imagery: baseline and comparative studies. 2003 IEEE Int. SOI Conf. Proc. (Cat. No.03CH37443) (2003).
Selinger, A. & Socolinsky, D. a. Face Recognition in the Dark. 2004 Conf. Comput. Vis. Pattern Recognit. Work. 0–5 (2004).
Socolinsky, D. & Selinger, A. A Comparative Analysis of Face Recognition Performance with Visible and Thermal Infrared Imagery. Proceedings. 16th Int. Conf. Pattern Recognition, 2002 4, 217–222 (2002).
Socolinsky, D.A. & Selinger, a. Thermal face recognition in an operational scenario. Proc. 2004 IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognition, 2004. CVPR 2004. 2, (2004).
Desa, S.M. & Hati, S. IR and visible face recognition using fusion of kernel based features. 2008 19th Int. Conf. Pattern Recognit. 1–4 (2008).
Vogiannou, A. et al. Advances in Biometrics. Adv. Biometrics 5558, pp. 838–846 (2009).
Guzman, A.M. et al. Thermal imaging as a biometrics approach to facial signature authentication. IEEE J. Biomed. Heal. Informatics 17, 214–222 (2013).
Zhou, Q., Li, Z. & Aggarwal, J. K. Boundary extraction in thermal images by edge map. Proc. 2004 ACM Symp. Appl. Comput. - SAC’04 254 (2004).
Lowe D.G., Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2), 91–110, (2004).
Perona, P. & Malik, J. Scale-space and edge detection using anisotropic diffusion. IEEE Transactions on Pattern Analysis and Machine Intelligence 12, 629–639 (1990).
Candocia, F. & Adjouadi, M. A similarity measure for stereo feature matching. IEEE Trans. Image Process. 6, 1460–1464 (1997).
Indian Thermal Face Database: http://cvprlab-sggs.co.in/index_files/Page2141.htm.
Dhamecha, T.I., Nigam, A, Singh, R. & Vatsa, M. Disguise detection and face recognition in visible and thermal spectrums. Biometrics (ICB), 2013 Int. Conf. 1–8 (2013).
Dhamecha, T.I., Singh, R., Vatsa, M. & Kumar, A. Recognizing disguised faces: Human and machine evaluation. PLoS One 9, (2014).
Dillencourt, M.B., Samet, H. & Tamminen, M. A general approach to connected-component labeling for arbitrary image representations. J. ACM 39, 253–280 (1992).
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Uke, S.R., Nandedkar, A.V. (2017). Thermal Face Recognition Using Face Localized Scale-Invariant Feature Transform. In: Raman, B., Kumar, S., Roy, P., Sen, D. (eds) Proceedings of International Conference on Computer Vision and Image Processing. Advances in Intelligent Systems and Computing, vol 459. Springer, Singapore. https://doi.org/10.1007/978-981-10-2104-6_54
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DOI: https://doi.org/10.1007/978-981-10-2104-6_54
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