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Algorithms for invariant long-wave infrared face segmentation: evaluation and comparison

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Abstract

This paper presents two methods for automatic segmentation of images of faces captured in long wavelength infrared, allowing a wide range of face rotations, expressions and artifacts (such as glasses and hats). We also present the validation of segmentation results using a recognition method to show the impact of the segmentation accuracy on the recognition. The paper presents two different approaches (one aimed at real-time performance and the other at high accuracy) and compares their performance against three other previously published methods. The proposed approaches are based on statistical modeling of pixel intensities and active contour application, although several other image processing operations are also performed. Experiments were performed on a total of 893 test images from four public available databases. The obtained results improve on previous existing methods up to 29.5 % for the first measure error (E 1) and up to 34.7 % for the second measure (E 2), depending on the method and database. Regarding the computational time, our proposals improve up to 63.32 % when compared with the other proposals. We also present the validation of the various segmentation methods that are presented by applying a face recognition method.

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References

  1. Akhloufi M, Bendada A, Batsale J (2008) State of the art in infrared face recognition. Quant Infrared Thermogr J 5(1):3–26

    Article  Google Scholar 

  2. Bowyer K, Chang K, Flynn P (2004) A survey of approaches to three-dimensional face recognition. 17th international conference on pattern recognition (ICPR 2004) 1:358–361

  3. Buddharaju P, Pavlidis I (2007) Multispectral face recognition: fusion of visual imagery with physiological information. In: Face biometrics for personal identification, chap. 7, Springer, Berlin, Heidelberg, pp 91–108

  4. Buddharaju P, Pavlidis I, Tsiamyrtzis P (2005) Physiology-based face recognition. In: IEEE conference on advanced video and signal based surveillance, pp 354–359

  5. Butakoff C, Frangi AF (2009) Multi-view face segmentation using fusion of statistical shape and appearance models. Comput Vis Image Underst 114(3):311–321

    Article  Google Scholar 

  6. Canny J (1986) A computational approach to edge detection. IEEE Trans PAMI 8(6):628–633

    Google Scholar 

  7. Chan TF, Vese LA (2001) Active contours without edges. IEEE Trans Image Process 10(2):266–277

    Article  MATH  Google Scholar 

  8. Chen X, Flynn P, Bowyer K (2005) IR and visible light face recognition. Comput Vis Image Underst 99:332–358

    Article  Google Scholar 

  9. Cho S, Wang L, Ong W (2009) Thermal imprint feature analysis for face recognition. IEEE Int Symp Ind Electron pp 1875–1880

  10. Filipe S, Alexandre LA (2010) Improving face segmentation in thermograms using image signatures. In: Proceedings of 15th Iberoamerican congress on pattern recognition. Lecture Notes in Computer Science. Springer, Berlin, Heidelberg, pp 402–409

  11. Flynn P, Bowyer K, Phillips P (2003) Assessment of time dependency in face recognition: an initial study. In: Proceedings of 4th international conference audio- and video-based biometric person authentication. Lecture Notes in Computer Science, vol 2688. Springer, Berlin, Heidelberg, pp 44–51

  12. Freedman D, Radke RJ, Lovelock DM, Chen GTY (2005) Model-based segmentation of medical imagery by matching distributions. IEEE Trans Med Imaging 24(3):281–292

    Article  Google Scholar 

  13. Gyaourova A, Bebis G, Pavlidis I (2004) Fusion of infrared and visible images for face recognition. In: Proceedings of 8th European conference on computer vision. Lecture Notes in Computer Science, vol 3024. Springer, Berlin, Heidelberg, pp 456–468

  14. Jain A, Flynn P, Ross A (2007) Handbook of Biometrics. Springer, New York

    Google Scholar 

  15. Kohavi R (1995) A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Proceedings of the 14th international joint conference on artificial intelligence-volume 2 (IJCAI’95). San Francisco, CA, USA, p 1137–1143

  16. Kong S, Heo J, Abidi B, Paik J, Abidi M (2005) Recent advances in visual and infrared face recognition: a review. Comput Vis Image Underst 97(1):103–135

    Article  Google Scholar 

  17. Mumford D, Shah J (1989) Optimal approximations by piecewise smooth functions and associated variational problems. Comm Pure Appl Math 42:577–685

    Article  MathSciNet  MATH  Google Scholar 

  18. Pantofaru C (2008) Studies in using image segmentation to improve object recognition. Ph.D. thesis, Robotics Institute, Carnegie Mellon University, Pittsburgh, PA

  19. Pavlidis I, Tsiamyrtzis P, Manohar C, Buddharaju P (2006) Biometrics: face recognition in thermal infrared. In: Biomedical engineering handbook, 3rd edn. chap 29, CRC Press, p 1–15

  20. Proença H, Alexandre L (2010) Iris recognition: analysis of the error rates regarding the accuracy of the segmentation stage. Image Vis Comput 28:202–206

    Article  Google Scholar 

  21. Ross A, Nandakumar K, Jain A (2006) Handbook of multibiometrics (international series on biometrics). Springer, New York

    Google Scholar 

  22. Segundo MP, Silva L, Bellon ORP, Queirolo CC (2010) Automatic face segmentation and facial landmark detection in range images. IEEE Trans Syst Man Cybern Part B Cybern 40(5):1319–1330

    Article  Google Scholar 

  23. Shah S, Abaza A, Ross A, Ammar H (2006) Automatic tooth segmentation using active contour without edges. In: 2006 biometrics symposium: special session on research at the biometric consortium conference, Baltimore, MD, p 1–6

  24. Sirovich L, Kirby M (1987) Low-dimensional procedure for the characterization of human faces. J Opt Soc Am A 4(3):519–524

    Article  Google Scholar 

  25. Sobottka K, Pitas I (1996) Segmentation and tracking of faces in color images. In: Second international conference on automatic face and gesture recognition, Killington, pp 236–241

  26. Srivastava A, Liu X (2003) Statistical hypothesis pruning for identifying faces from infrared images. Image Vis Comput 21:651–661

    Article  Google Scholar 

  27. Turk M, Pentland A (1991) Eigenfaces for recognition. J Cognit Neurosci 3:71–86

    Article  Google Scholar 

  28. Turk M, Pentland A (1991) Face recognition using eigenfaces. In: IEEE conference on computer vision and pattern recognition (CVPR’91), Maui, HI, USA, pp 586–591

  29. Vese LA, Chan TF (2002) A multiphase level set framework for image segmentation using the Mumford and Shah model. Int J Comput Vis 50(3):271–293

    Article  MATH  Google Scholar 

  30. OTCBVS WS Series Bench, I.: http://www.cse.ohio-state.edu/otcbvs-bench/. DOE University Research Program in Robotics under grant DOE-DE-FG02-86NE37968; DOD/TACOM/NAC/ARC Program under grant R01-1344-18; FAA/NSSA grant R01-1344-48/49; Office of Naval Research under grant #N000143010022.3

  31. OTCBVS WS Series Bench; Roland Miezianko, I.: Terravic research infrared database. http://www.cse.ohio-state.edu/otcbvs-bench/

  32. Zhang Y, Zhou Z (2010) Cost-sensitive face recognition. IEEE Trans Pattern Anal Mach Intell 32(10):1758–1769

    Article  Google Scholar 

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Acknowledgments

We thank the anonymous reviewers for insightful comments that considerably strengthened the presentation of this work. We wish to thank Professor Cho Siu-Yeung David from the School of Computer Engineering at Nanyang Technological University (NTU) for the source code of his method [9]. We acknowledge the financial support given by ‘FCT - Fundação para a Ciência e Tecnologia’ and ‘FEDER’ in the scope of the PTDC/EIA/69106/2006 research project ‘BIOREC: Non-Cooperative Biometric Recognition’, the PTDC/EIA-EIA/103945/2008 research project ‘NECOVID: Covert Negative Biometric Identification’ and in the scope of the research grant SFRH/BD/72575/2010. We also acknowledge the support given by the IT - Instituto de Telecomunicações through ‘PEst-OE/EEI/LA0008/2013’.

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Correspondence to Sílvio Filipe.

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Filipe, S., Alexandre, L.A. Algorithms for invariant long-wave infrared face segmentation: evaluation and comparison. Pattern Anal Applic 17, 823–837 (2014). https://doi.org/10.1007/s10044-013-0354-6

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