Abstract
In this paper an efficient approach for human face recognition based on the use of minutiae points is proposed. The thermogram of human face is captured by thermal infra-red camera. Image processing technologies are used to pre-process the captured thermogram. Then different physiological features are extracted using bit-plane slicing from the captured thermogram. These extracted features are called blood perfusion data. Blood perfusion data are characterized by the regional blood flow in human tissue and therefore do not depend entirely on surrounding temperature. These data bear a great potential for deriving discriminating facial thermogram for better classification and recognition of face images in comparison to static image data. Blood perfusion data are related to distribution of blood vessels under the face skin. Distribution of blood vessels is unique for each person and as set of extracted minutiae points from a blood perfusion data of a human face should be unique for that face. There may be several such minutiae point sets for a single face but all of these correspond to that particular face only. Entire face image is partitioned into equal consequence blocks and the total number of minutiae points from each block is computed to construct final vector. Therefore, the size of the feature vectors is found to be same as total number of blocks considered. A five layer feed-forward back propagation neural network is used as the classification tool. A number of experiments were conducted to evaluate the performance of the proposed face recognition system with varying block size. Experiments have been performed on the database created at our own laboratory. The maximum success of 95.24% recognition has been achieved with block size 8×8 and 32×32 with bit-plane 4 and accuracy rate of 97.62% has been achieved with block size 16×16 for bit-plane 4.
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References
Toth, B.: Biometric Liveness Detection. Information Security Bulletin (October 2005)
Jain, A.K., Flynn, P., Ross, A.A.: Handbook of Biometrics. Springer (2007)
Socolinsky, D., Selinger, A.: Face recognition with visible and thermal infrared imagery (2003)
Wu, S., Fang, Z.-J., Xie, Z.-H., Liang, W.: Blood Perfusion Models for Infrared Face Recognition. School of information technology, Jiangxi University of Finance and Economics, China
Prokoski, F.: History, current status, and future of infrared identification. In: Proceedings of the IEEE Workshop Computer Vision Beyond Visible Spectrum: Methods and Applications, pp. 5–14 (2000)
Kong, S.G., Heo, J., Abidi, B.R., Paik, J., Abidi, M.A.: Recent advances in visual and infrared face recognition: a review. Comput. Vision Image Understanding 97, 103–135 (2005)
Chen, X., Flynn, P.J., Bowyer, K.W.: IR and Visible light face Recognition. University of NotreDame, USA (2005)
Wu, S.Q., Gu, Z.H., Chia, K.A., Ong, S.H.: Infrared facial recognition using modified blood perfusion. In: Proceedings 6th Int. Conf. Inform., Comm. & Sign. Proc., Singapore, pp. 1–5 (December 2007)
Heo, J., Savvides, M., Vijayakumar, B.V.K.: Performance Evaluation of Face Recognition using Visual and Thermal Imagery with Advanced Correlation Filters. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005 (2005)
Jones, B.F., Plassmann, P.: Digital infrared thermal imaging of human skin. IEEE Engineering in Medicine & Biology Magazine 21(6), 41–48 (2002)
Manohar, C.: Extraction of Superficial Vasculature in Thermal Imaging. Master’S Thesis, Dept. Electrical Eng., Univ. of Houston, Houston, Texas (December 2004)
Guyton, A.C., Hall, J.E.: Textbook of Medical Physiology, 9th edn. W.B. Saunders Company, Philadelphia (1996)
De Geef, S., Claes, P., Vandermeulen, D., Mollemans, W., Willems, P.G.: Large-Scale In-Vivo Caucasian Facial Soft Tissue Thickness Database for Craniofacial Reconstruction. Forensic Science 159(1), S126–S146 (2006)
Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 3rd edn. Prentice-Hall (2002)
Galton, F.: Finger Prints. Mcmillan, London (1892)
Maltoni, D., Maio, D., Jain, A.K., Prabhakar, S.: Handbook of Fingerprint Recognition, 2nd edn. Springer-Verlag London Limited (2009)
Jain, A., Ross, A., Prabhakar, S.: Fingerprint matching using minutiae and texture features. In: Proc. of Intl. Conf. on Image Processing, ICIP, Thessaloniki, Greece, October 7-10, pp. 282–285 (2001)
Turk, M., Pentland, A.: Eigenfaces for recognition. Journal of Cognitive Neuroscience 3(1) (1991)
Turk, M., Pentland, A.: Face recognition using eigenfaces. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition, pp. 586–591 (1991)
Lin, Lee: Neural Fuzzy Systems. Prentice Hall International (1996)
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Seal, A., Bhattacharjee, D., Nasipuri, M., Basu, D.K. (2012). Minutiae from Bit-Plane Sliced Thermal Images for Human Face Recognition. In: Deep, K., Nagar, A., Pant, M., Bansal, J. (eds) Proceedings of the International Conference on Soft Computing for Problem Solving (SocProS 2011) December 20-22, 2011. Advances in Intelligent and Soft Computing, vol 131. Springer, New Delhi. https://doi.org/10.1007/978-81-322-0491-6_11
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DOI: https://doi.org/10.1007/978-81-322-0491-6_11
Publisher Name: Springer, New Delhi
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