Advertisement

Multimedia Tools and Applications

, Volume 74, Issue 9, pp 2913–2937 | Cite as

UGC-JU face database and its benchmarking using linear regression classifier

  • Ayan Seal
  • Debotosh Bhattacharjee
  • Mita Nasipuri
  • Dipak Kr. Basu
Article

Abstract

In this paper, a new face database has been presented which will be freely available to academicians and research community for research purposes. The face database consists of both visual and thermal face images of 84 persons with varying poses, expressions and occlusions (39 different variations for each type, visual or thermal). A new thermal face image recognition technique based on Gappy Principal Component Analysis and Linear Regression Classifier has also been presented here. The recognition performance of this technique on the thermal face images of this database is found to be 98.61 %, which can be considered as the initial benchmark recognition performance this database.

Keywords

Thermal face images Visual images Face database GappyPCA LRC classifier Decision level fusion 

Notes

Acknowledgments

Authors are thankful to a project entitled “Development of 3D Face Recognition Techniques Based on Range Images,” funded by Deity, Govt. of India and “DST-PURSE Programme” at Department of Computer Science and Engineering, Jadavpur University, India for providing necessary infrastructure to conduct experiments relating to this work. Ayan Seal is grateful to Department of Science & Technology (DST), Govt. of India for providing him Junior Research Fellowship-Professional (JRF-Professional) under DST-INSPIRE Fellowship programme [No: IF110591].

References

  1. 1.
    Alyüz N, Gökberk B, Dibeklioğlu H, Savran A, Salah AA, Akarun L, Sankur B. “3D face recognition benchmarks on the bosphorus database with focus on facial expressions”. The First COST 2101 Workshop on Biometrics and Identity Management (BIOID 2008), Roskilde University, Denmark, pp 1–10, May 2008Google Scholar
  2. 2.
    Balci K, Atalay V (2002) “PCA for gender estimation: which eigenvectors contribute?” In: Proceedings of Sixteenth International Conference on Pattern Recognition, vol. 3. Quebec City, Canada, pp 363–366Google Scholar
  3. 3.
    Barsi R, Jacobs D (2003) Lambertian reflection and linear subspaces. IEEE Trans Pattern Anal Mach Intell 25(2):218–233CrossRefGoogle Scholar
  4. 4.
    Belhumeur P, Hespanha J, Kriegman D (1997) Eigenfaces vs. fisherfaces: class specific linear projection. IEEE Trans Pattern Anal Mach Intell 19(7):711–720CrossRefGoogle Scholar
  5. 5.
    Belhumeur PN, Hespanha JP, Kriegman DJ (1997) Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Intell 19(7):711–720CrossRefGoogle Scholar
  6. 6.
    Bhattacharjee D, Seal A, Ganguly S, Nasipuri M, Basu DK (2012) “A comparative study of human thermal face recognition based on Haar wavelet transform (HWT) and Local Binary Pattern (LBP)”. Comput Intell Neurosci:1–12Google Scholar
  7. 7.
    Bhowmik MK, Bhattacharjee D, Nasipuri M, Basu DK, Kundu M. “Classification of polar-thermal eigenfaces using multilayer perceptron for human face recognition”. In: Proceedings of the 3rd IEEE Conference on Industrial and Information Systems (ICIIS-2008), pp 118, Dec. 8–10, 2008Google Scholar
  8. 8.
    Bronstein A, Bronstein M, Gordon E, Kimmel R (2003) “Expression-invariant 3D face recognition.” Audio and Video based Person Authentication (AVBPA 2003), LCNS 2688, J. Kittler and M. Nixon, eds., pp 62–70Google Scholar
  9. 9.
    Brunelli R, Poggio T (1992) “HyperBF networks for gender classification.” Proceedings of DARPA Image Understanding Workshop, pp 311–314Google Scholar
  10. 10.
    Buddharaju P, Pavlidis I, Kakadiaris I. “Face recognition in the thermal infrared spectrum”. Proceedings of the 2004 I.E. Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW’04)Google Scholar
  11. 11.
    Chen X, Flynn PJ, Bowyer KW (2003) PCA-based face recognition in infrared imagery: baseline and comparative studies. Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures (AMFG’03), 2003Google Scholar
  12. 12.
    Chen Y-T, Wang M-S (2002) Human face recognition using thermal image. J Med Biol Eng 22(2):97–102Google Scholar
  13. 13.
    Colmenarez A, Frey BJ, Huang TS (1999) “A probabilistic framework for embedded face and facial expression recognition”. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol. 1. Ft. Collins, CO, USA, pp 1592–1597Google Scholar
  14. 14.
    Cutler R. “Face recognition using infrared images and eigenfaces,” http://citeseer.ist.psu.edu/cutler96face.html, April, 1996, visited July 2007
  15. 15.
    Everson R, Sirovich L (1995) “Karhunen-Loeve procedure for gappy data.” J Opt Soc Am A:1657–1664Google Scholar
  16. 16.
    Gong S, Mckenna SJ, Psarrou A (2000) “Dynamic vision: from images to face recognition”. ImperCooege PressGoogle Scholar
  17. 17.
    Gonzalez RC, Woods RE. Digital image processing, 3rd edn. Prentice HallGoogle Scholar
  18. 18.
    Hastie T, Tibshirani R, Friedman J (2001) The elements of statistical learning; data mining, inference and prediction. SpringerGoogle Scholar
  19. 19.
    Hearn D, Baker MP. Computer graphics C version, 2nd edn. Prentice HallGoogle Scholar
  20. 20.
    Hermosilla G, Loncomilla G, Ruiz-del-Solar J. “Thermal face recognition using local interest points and descriptors for HRI applications” Computer Vision and Pattern Recognition Workshops (CVPRW), IEEE Computer Society Conference, June 2010Google Scholar
  21. 21.
    Hermosilla G, Loncomilla P, Ruiz-del-Solar J. “Thermal face recognition using local interest points and descriptors for HRI applications” Computer Vision and Pattern Recognition Workshops (CVPRW), IEEE Computer Society, pp 25–35, June 2010Google Scholar
  22. 22.
    Intelligent Multimedia Lab (2001) “Asian face image database PF01”, Technical Report (department of Computer Science and Engineering, Pohang University of Science and Technology, Korea), pp 1–9Google Scholar
  23. 23.
    Kim K (2005) “Intelligent immigration control system by using passport recognition and face verification”. In: International Symposium on Neural Networks. Chongqing, China, pp 147–156Google Scholar
  24. 24.
    Leonardis A, Bischof H (2000) Robust recognition using eigenimages. Comput Vis Image Underst 78(1):99–118CrossRefGoogle Scholar
  25. 25.
    Li J, Hao P, Zhang C, Dou M (2008) “Hallucinating faces from thermal infrared images”. Proceedings of Image Processing, 2008. ICIP 2008. 15th IEEE International Conference on San Diego, CA, pp 465–468, doi: 10.1109/ICIP.2008.4711792
  26. 26.
    Liu JNK, Wang M, Feng B (2005) iBotGuard: an internet-based intelligent robot security system using invariant face recognition against intruder. IEEE Trans Syst Man Cybern Part C Appl Rev 35:97–105CrossRefGoogle Scholar
  27. 27.
    Martinez AM, Benavente R. “The AR face database”. CVC Technical Report #24, June 1998Google Scholar
  28. 28.
    Martinez B, Binefa X, Pantic M (2010) “Facial component detection in thermal imagery”. IEEE computer society conference on computer vision and pattern recognition workshops(CVPRW), pp 48–52Google Scholar
  29. 29.
    Messar K, Matas J, Kittler J, Luettin J, Maitre G. “Xm2vtsdb: the extended m2vts database”. Second International Conference on Audio and video-based Biometric Person Authentication, pp 72–77, March 1999Google Scholar
  30. 30.
    Moghaddam B, Yang MH (2002) Learning gender with support faces. IEEE Trans Pattern Anal Mach Intell 24:707–711CrossRefGoogle Scholar
  31. 31.
    Moon H (2004) “Biometrics person authentication using projection-based face recognition system in verification scenario”. In: International conference on bioinformatics and its applications. Hong Kong, China, pp 207–213Google Scholar
  32. 32.
    Morse BS. Lecture 2: “image processing review, neighbors, connected components, and distance”, 1998–2004Google Scholar
  33. 33.
    Naseem I, Togneri R, Bennamoun M (2010) Linear regression for face recognition. IEEE Trans Pattern Anal Mach Intell (IEEE TPAMI) 32(11):2106–2112CrossRefGoogle Scholar
  34. 34.
    Phillips PJ, Moon H, Rauss PJ, Rizvi S (2000) The FERET evaluation methodology for face recognition algorithms. IEEE Trans Pattern Anal Mach Intell 22(10):1090–1104CrossRefGoogle Scholar
  35. 35.
    Prokoski F (2000) History, current status, and future of infrared identification. In: Proceedings of the IEEE Workshop Computer Vision Beyond Visible Spectrum: Methods and Applications. pp 5–14Google Scholar
  36. 36.
    Prokoski FJ, Riedel RB, Coffin JS (1992) “Identification of individuals by means of facial thermography.” In: Proceedings of the IEEE 1992 International Carnahan Conference on Security Technology: Crime Countermeasures, Atlanta, GA, USA 14–16 Oct., pp 120–125, IEEEGoogle Scholar
  37. 37.
    Ryan TP (1997) Modern regression methods. Wiley-InterscienceGoogle Scholar
  38. 38.
    Samaria F, Harter A (1994) “Parameterisation of a stochastic model for human face identification”. Proc. of 2nd IEEE Workshop on Applications of Computer Vision, Sarasota, FL, pp 138–142Google Scholar
  39. 39.
    Seal A, Ganguly S, Bhattacharjee D, Nasipuri M, Basu DK (2013) Automated thermal face recognition based on minutiae extraction. Int J Comput Intell Stud 2(2):133–156CrossRefGoogle Scholar
  40. 40.
    Seber GAF (2003) Linear regression analysis. Wiley-InterscienceGoogle Scholar
  41. 41.
    Selinger A, Socolinsky DA. “Appearance- based facial recognition using visible and thermal imagery: a comparative study.” Equinox Corporation, Technical Report 02-01 February 2002Google Scholar
  42. 42.
    OTCBVS WS Series Bench; Davis J, Keck M. “A two stage template approach to person detection in thermal imagery.” In: Proc. Workshop on Applications of Computer Vision, pp 364–369, Jan., 2005Google Scholar
  43. 43.
    Shinohara Y, Otsu N (2004) “Facial expression recognition using fisher weight maps”. In: Sixth IEEE International Conference on Automatic Face and Gesture Recognition, vol. 100, pp 499–504Google Scholar
  44. 44.
    Socolinsky D, Wolff L, Neuheisel J, Eveland C (2001) “Illumination invariant face recognition using thermal infrared imagery.” In: IEEE Computer Society International Conference on Computer Vision and Pattern Recognition, vol. 1. Kauai, HI, USA, pp 527–534Google Scholar
  45. 45.
    Staudte RG, Sheather SJ (1990) Robust estimation and testing. Wiley-InterscienceGoogle Scholar
  46. 46.
    Trujillo L, Olague G, Hammoud R, Hernandez B (2005) Automatic feature localization in thermal images for facial expression recognition. Proceeding of CVPR’05 Proceedings of the 2005 I.E. Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), ISBN:0-7695-2372-2-3, Washington, DC, USA, 2005Google Scholar
  47. 47.
    Trujillo L, Olague G, Hammoud R, Hernandez B (2005) Automatic feature localization in thermal images for facial expression recognition, Proceedings of the 2005 I.E. Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), p 14, ISBN:0-7695-2372-2-3, Washington, DC, USA, 2005Google Scholar
  48. 48.
    Turk M, Pentland A (1991) Eigenfaces for recognition. J Cogn Neurosci 3(1):71–86CrossRefGoogle Scholar
  49. 49.
    Venkatesan S, Madane SSR (2010) Face recognition system with genetic algorithm and ANT colony optimization. Int J Innov Manag Technol 1(5):469–471Google Scholar
  50. 50.
    Wright J, Ma Y, Mairal J, Sapiro G, Huang TS, Yan S (2010) Sparse representation for computer vision and pattern recognition. Proc IEEE 98(6):1031–1044CrossRefGoogle Scholar
  51. 51.
    Yoshitomi Y, Miyaura T, Tomita S, Kimura S (1997) Face identification using thermal image processing. Proc IEEE Int Work Robot Hum Commun:374–379Google Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Ayan Seal
    • 1
  • Debotosh Bhattacharjee
    • 2
  • Mita Nasipuri
    • 2
  • Dipak Kr. Basu
    • 2
  1. 1.DST INSPIRE Fellow, Department of Computer Science and EngineeringJadavpur UniversityKolkataIndia
  2. 2.Department of Computer Science and EngineeringJadavpur UniversityKolkataIndia

Personalised recommendations