Abstract
Infrared (IR) face recognition is getting wide attention with its increased number of applications as it provides numerous advantages over visual face recognition. As IR images are invariant to different illumination conditions they can provide robust thermal characteristics. The paper proposes a thermal IR based face recognition system using Zernike moments ZM and Multi Layer Perceptron Neural Network. The recognition experiment was performed using the images obtained from Terravic Facial IR Database with variations in poses (front, left and right) and environments (indoor/outdoor). The proposed method shows that the combination of magnitudes of ZM obtained from orders zero to two as feature vector provides the best average recognition accuracy of 89.5% and false acceptance rate of 0.356%.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Introna, L.D., Nissenbaum, H.: Facial recognition technology: a survey of policy and implementation issues. Center for Catastrophe Preparedness and Response, New York University (2009)
Chellappa, R., Wilson, C.L., Sirohey, S.: Human and machine recognition of faces: a survey. Proc. IEEE 83(5), 705–741 (1995)
Zhao, W., Chellappa, R., Phillips, P.J., Rosenfeld, A.: Face recognition: a literature survey. ACM Comput. Surv. (CSUR) 35(4), 399–458 (2003)
Kong, S.G., Heo, J., Abidi, B.R., Paik, J., Abidi, M.A.: Recent advances in visual and infrared face recognition—a review. Comput. Vis. Image Underst. 97(1), 103–135 (2005)
Bhowmik, M.K., Saha, K., Majumder, S., Majumder, G., Saha, A., Sarma, A.N., Bhattacharjee, D., Basu, D.K., Nasipuri, M.: Thermal infrared face recognition–a biometric identification technique for robust security system. In: Reviews, Refinements and New Ideas in Face Recognition. InTech (2011)
Wilder, J., Phillips, P.J., Jiang, C., Wiener, S.: Comparison of visible and infra-red imagery for face recognition. In: 1996 Proceedings of the Second International Conference on Automatic Face and Gesture Recognition, pp. 182–187. IEEE, October 1996
Singh, S., Gyaourova, A., Bebis, G., Pavlidis, I.: Infrared and visible image fusion for face recognition. In: Defense and Security, pp. 585–596. International Society for Optics and Photonics, August 2004
Miller, J.L.: Principles of Infrared Technology. Kluwer, Dordrecht (1994)
Jiuqing, W., Xingshan, L.: PCB infrared thermal imaging diagnosis using support vector classifier. In: 2002 Proceedings of the 4th World Congress on Intelligent Control and Automation, vol. 4, pp. 2718–2722. IEEE (2002)
Lin, K.C., Chen, W.L., Chen, S.C., Wang, F.S.: Diagnostic technique for classifying the quality of circuit boards using infrared thermal image. In 2001 Joint 9th IFSA World Congress and 20th NAFIPS International Conference, vol. 1, pp. 464–469. IEEE, July 2001
Shirvaikar, M.V., Trivedi, M.M.: A neural network filter to detect small targets in high clutter backgrounds. IEEE Trans. Neural Networks 6(1), 252–257 (1995)
Foster, K.R.: Thermographic detection of breast cancer. IEEE Eng. Med. Biol. Mag. 17(6), 10–14 (1998)
Pavlidis, I., Levine, J., Baukol, P.: Thermal image analysis for anxiety detection. In: 2001 Proceedings of the International Conference on Image Processing, vol. 2, pp. 315–318. IEEE, October 2001
Li, S.Z., Zhang, L., Liao, S., Zhu, X., Chu, R., Ao, M., He, R.: A near-infrared image based face recognition system. In: FG, pp. 455–460, April 2006
Maeng, H., Choi, H.C., Park, U., Lee, S.W., Jain, A.K.: NFRAD: Near-infrared face recognition at a distance. In: 2011 International Joint Conference on Biometrics (IJCB), pp. 1–7. IEEE, October 2011
Chen, X., Jing, Z.: Infrared face recognition based on Log-Gabor wavelets. Int. J. Pattern Recognit. Artif. Intell. 20(03), 351–360 (2006)
Alasag, T., Gokmen, M.: Face Recognition in Low-resolution Images by Using Local Zernike Moments (2014)
Bebis, G., Gyaourova, A., Singh, S., Pavlidis, I.: Face recognition by fusing thermal infrared and visible imagery. Image Vis. Comput. 24(7), 727–742 (2006)
Khotanzad, A., Hong, Y.H.: Invariant image recognition by Zernike moments. IEEE Trans. Pattern Anal. Mach. Intell. 12(5), 489–497 (1990)
Tahmasbi, A., Saki, F., Shokouhi, S.B.: Classification of benign and malignant masses based on Zernike moments. Comput. Biol. Med. 41(8), 726–735 (2011)
Vorobyov, M.: Shape classification using zernike moments. Technical report. iCamp-University of California Irvine (2011)
Zhenjiang, M.: Zernike moment-based image shape analysis and its application. Pattern Recogn. Lett. 21(2), 169–177 (2000)
Park, S.B., Lee, J.W., Kim, S.K.: Content-based image classification using a neural network. Pattern Recogn. Lett. 25(3), 287–300 (2004)
Dougherty, G.: Pattern Recognition and Classification. Springer, New York (2013). ISBN 978-1-4614-5323-9 (eBook)
Fausett, L.V.: Fundamentals of Neural Networks. Prentice-Hall, Englewood Cliffs (1994)
IEEE OTCBVS WS Series Bench, Miezianko, R.: Terravic Research Infrared Database (2014)
Larose, D.T.: Discovering Knowledge in Data: An Introduction to Data Mining. Wiley, Hoboken (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Mahesh, V.G.V., Joseph Raj, A.N., Arulmozhivarman, P. (2018). Thermal IR Face Recognition Using Zernike Moments and Multi Layer Perceptron Neural Network (MLPNN) Classifier. In: Abraham, A., Cherukuri, A., Madureira, A., Muda, A. (eds) Proceedings of the Eighth International Conference on Soft Computing and Pattern Recognition (SoCPaR 2016). SoCPaR 2016. Advances in Intelligent Systems and Computing, vol 614. Springer, Cham. https://doi.org/10.1007/978-3-319-60618-7_21
Download citation
DOI: https://doi.org/10.1007/978-3-319-60618-7_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-60617-0
Online ISBN: 978-3-319-60618-7
eBook Packages: EngineeringEngineering (R0)