Advertisement

Local Operators and Measures for Heterogeneous Face Recognition

Chapter

Abstract

This chapter provides a summary of local operators recently proposed for heterogeneous face recognition . It also analyzes performance of each individual operator and demonstrates performance of composite operators. Basic local operators include local binary patterns (LBP), generalized local binary patterns (GLBPs), Weber local descriptors (WLDs), Gabor filters, and histograms of oriented gradients (HOGs). They are directly applied to normalized face images. The composite operators include Gabor filters followed by LBP, Gabor filters followed by WLD, Gabor filters followed by GLBP, Gabor filters followed by LBP, GLBP and WLD, Gabor ordinal measures (GOM), and composite multi-lobe descriptors (CMLD). When applying a composite operator to face images, images are first normalized and processed with a bank of Gabor filters and then local operators or combinations of local operators are applied to the outputs of Gabor filters. After a face image is encoded using the local operators, the outputs of local operators are converted to a histogram representation and then concatenated, resulting in a very long feature vector. No effective dimensionality reduction method or feature selection method has been found to reduce the size of the feature vector. Each component in the feature vector appears to contribute a small amount of information needed to generate a high fidelity matching score. A matching score is generated by means of Kullback-Leibler distance between two feature vectors. The cross-matching performance of heterogeneous face images is demonstrated on two datasets composed of active infrared and visible light face images. Both short and long standoff distances are considered.

Keywords

Feature Vector Face Recognition Linear Discriminant Analysis Independent Component Analysis Local Operator 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

The authors would like to thank Brian Lemoff of West Virginia High Technology Consortium Foundation for providing the Pre-TINDERS and TINDERS datasets employed in the described experiments in this book chapter.

References

  1. 1.
    Ahonen, T., Hadid, A., Pietikainen, M.: Face recognition with local binary patterns. In: Proceedings of European Conference on Comuputer Vision (ECCV), pp. 469–481 (2004)Google Scholar
  2. 2.
    Akhloufi, M., Bendada, A.H.: Multispectral infrared face recognition: a comparative study. In: Proceedings of Quantitative InfraRed Thermography (2010)Google Scholar
  3. 3.
    Bartlet, M.S., Sejnowski, T.J.: Independent components of face images: a representation for face recognition. In: Proceedings of 4th Annual Journal Symposium Neural Computation (1997)Google Scholar
  4. 4.
    Bartlett, M.S., Movellan, J.R., Sejnowski, T.J.: Face recognition by independent component analysis. IEEE Trans. Neural Netw. 13(6), 14501464 (2002)Google Scholar
  5. 5.
    Bourlai, T., Kalka, N., Ross, A., Cukic, B., Hornak, L.: Cross-spectral face verification in the short wave infrared (SWIR) band. In: Proceedings of International Conference on Patterns Recognition, pp. 1343–1347 (2010)Google Scholar
  6. 6.
    Buddharaju, P., Pavlidis, I.T., Tsiamyrtzis, P., Bazakos, M.: Physiology-based face recognition in the thermal infrared spectrum. IEEE Trans. Pattern Anal. Machine Intell. 29(4), 613–626 (2007)CrossRefGoogle Scholar
  7. 7.
    Cao, Z., Schmid, N.A.: Recognition performance of cross-spectral periocular biometrics and partial face at short and long standoff distance. Open Trans. Info. Process. 1(2), 20–32Google Scholar
  8. 8.
    Cao, Z., Schmid, N.A.: Matching heterogeneous periocular regions: short and long standoff distances. In: Proceedings of the IEEE International Conference on Image Processing, pp. 4967–4971 (2014)Google Scholar
  9. 9.
    Cao, Z., Schmid, N.A.: Composite multi-lobe descriptor for cross-spectral face recognition: matching active ir to visible light images. In: Proc. SPIE. 9476, pp. 94,760T–94,760T–13 (2015)Google Scholar
  10. 10.
    Cao, Z., Schmid, N.A.: Fusion of operators for heterogeneous periocular recognition at varying ranges. Pattern Recogn. Lett. doi:  10.1016/j.patrec.2015.10.018. http://www.sciencedirect.com/science/article/pii/S0167865515003694. (2015)Google Scholar
  11. 11.
    Chai, Z., Sun, Z., Mendez-Vazquez, H., He, R., Tan, T.: Gabor ordinal measures for face recognition. IEEE Trans. Info. Forensics Secur. 9(1), 14–26 (2014)Google Scholar
  12. 12.
    Chen, J., Shan, S., He, C., Zhao, G., Pietikeinen, M., Chen, X., Gao, W.: Wld: A robust local image descriptor. IEEE Trans. Pattern Anal. Mach. Int. 32(9), 1705–1720 (2010)Google Scholar
  13. 13.
    Chen, X., Flynn, P., Bowyer, K.: PCA-based face recognition in infrared imagery: Baseline and comparative studies. In: Proceedings of IEEE International Workshop on Analysis and Modeling of Faces and Gestures, pp. 127–134 (2003)Google Scholar
  14. 14.
    Chen, X., Flynn, P.J., Bowyer, K.W.: IR and visible light face recognition. Comput. Vis. Image Understand. 99(3), 332–358 (2005)CrossRefGoogle Scholar
  15. 15.
    Cognitec: Facevacs software developer kit cognitec systems. (Online) http://www.cognitec-systems.de. Accessed 04 Jan 2015
  16. 16.
    Comon, P.: Independent component analysis, a new concept? Sig. Process. 36(3), 287–314 (1994)CrossRefzbMATHGoogle Scholar
  17. 17.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 886–893 (2005)Google Scholar
  18. 18.
    Daugman, J.G.: Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. J. Opt. Soc. Am. A 2(7), 11601169 (1985)Google Scholar
  19. 19.
    Daugman, J.G.: Complete discrete 2-d Gabor transforms by neural networks for image analysis and compression. IEEE Trans. Pattern Anal. Machine Intell. 36(7), 1169–1179 (1988)zbMATHGoogle Scholar
  20. 20.
    Fisher, R.A.: The use of multiple measurements in taxonomic problems. Ann. Eugenics 7(2), 179–188 (1936)CrossRefGoogle Scholar
  21. 21.
    Goodrich: Surveillance using SWIR night vision cameras. (online) http://www.sensorsinc.com/facilitysecurity.html. Accessed 01 Feb , 2015
  22. 22.
    Guo, Y., Xu, Z.: Local Gabor phase difference pattern for face recognition. In: Proceedings of International Conference on Pattern Recognition, pp. 1–4 (2008)Google Scholar
  23. 23.
    Hansen, M.P., Malchow, D.S.: Overview of SWIR detectors, cameras, and applications. In: Proceedings of SPIE: Thermosense XXX, pp. 69, 390I–69, 390I–11 (2008)Google Scholar
  24. 24.
    Heikkil, M., Pietikinen, M.: A texture-based method for modeling the background and detecting moving objects. IEEE Trans. Pattern Anal. Mach. Intell. 28(4), 657–662 (2006)Google Scholar
  25. 25.
    Hotelling, H.: Relations between two sets of variates. Biometrika 28(3), 321–377 (1936)MathSciNetCrossRefzbMATHGoogle Scholar
  26. 26.
    Jain, A.K., Ratha, N.K., Lakshmanan, S.: Object detection using Gabor filters. Pattern Recogn. 30(2), 295309 (1997)Google Scholar
  27. 27.
    Jutten, C., Herault, J.: Blind separation of sources i. an adaptive algorithm based on neuromimetic architecture. Signal Process. 24(1), 110 (1991)Google Scholar
  28. 28.
    Kirschner, J.: SWIR for target detection, recognition, and identification. (online) http://www.photonicsonline.com/doc.mvc/SWIR-For-Target-Detection-Recognition-And-0002 (2011). Accessed 04 Jan 2015
  29. 29.
    Klare, B., Jain, A.K.: Heterogeneous face recognition: matching NIR to visible light images. In: Proceedings of International Conference on Pattern Recognition, pp. 1513–1516 (2010)Google Scholar
  30. 30.
    Kullback, S., Leibler, R.A.: On information and sufficiency. Ann. Math. Stat. 22(1), 79–86 (1951)MathSciNetCrossRefzbMATHGoogle Scholar
  31. 31.
    Lee, T.S.: Image representation using 2d Gabor wavelets. IEEE Trans. Pattern Anal. Mach. Intell. 18(10), 959971 (1996)Google Scholar
  32. 32.
    Li, S.Z., Chu, R., Liao, S., Zhang, L.: Illumination invariant face recognition using near-infrared images. IEEE Trans. Pattern Anal. Mach. Intell. 29(4), 627–639 (2007)CrossRefGoogle Scholar
  33. 33.
    Lyons, M., Akamatsu, S., Kamachi, M., Gyoba, J.: Coding facial expressions with Gabor wavelets. In: Proceedings of IEEE International Conference on Automatic Face and Gesture Recognition, pp. 200–205 (1998)Google Scholar
  34. 34.
    Marelja, S.: Mathematical description of the responses of simple cortical cells. J. Opt. Soc. Am. 70(11), 12971300 (1980)Google Scholar
  35. 35.
    Martin, R.B., Kafka, K.M., Lemoff, B.E.: Active-SWIR signatures for long-range night/day human detection and identification. In: Proceedings of the SPIE Symposium on DSS, pp. 209–218 (2013)Google Scholar
  36. 36.
    Mehrotra, R., Namuduri, K.R., Ranganathan, N.: Gabor filter-based edge detection. Pattern Recognition 25(12), 14791494 (1992)Google Scholar
  37. 37.
    Melzera, T., Reitera, M., Bisch, H.: Appearance models based on kernel canonical correlation analysis. Pattern Recogn. 36(9), 1961–1971 (2003)CrossRefGoogle Scholar
  38. 38.
    Nicolo, F.: Homogeneous and heterogeneous face recognition: enhancing, encoding and matching for practical applications. Ph.D. thesis, West Virginia University (2012)Google Scholar
  39. 39.
    Nicolo, F., Schmid, N.A.: A method for robust multispectral face recognition. In: Proceedings of the International Conference on Image Analysis and Recognition, pp. 180–190 (2011)Google Scholar
  40. 40.
    Nicolo, F., Schmid, N.A.: Long range cross-spectral face recognition: Matching SWIR against visible light images. IEEE Trans. Inf. Forensics Secur. 7(6), 1717–1726 (2012)Google Scholar
  41. 41.
    Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Inf. Forensics Secur. 24(7), 971–987 (2002)zbMATHGoogle Scholar
  42. 42.
    Ojala, T., Pietikinen, M., Harwood, D.: Performance evaluation of texture measures with classification based on Kullback discrimination of distributions. In: Proceedings of IAPR International Conference on Pattern Recognition, pp. 582–556 (1994)Google Scholar
  43. 43.
    Ross, H.E., Murray, D.J.: E. H. Weber on the Tactile Senses, 2nd edn. Erlbaum (UK) Taylor and Francis (1996)Google Scholar
  44. 44.
    Sirovich, L., Kirby, M.: Low-dimensional procedure for the characterization of human faces. J. Opt. Soc. Am. A 4(3), 519–524 (1987)CrossRefGoogle Scholar
  45. 45.
    Socolinsky, D., Wolff, L., Neuheisel, J., Eveland, C.: Illumination invariant face recognition using thermal infrared imagery. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 527–534 (2001)Google Scholar
  46. 46.
    Stevens, S.S.: On the theory of scales of measurement. Science 103(2684), 677–680 (1946)CrossRefzbMATHGoogle Scholar
  47. 47.
    Turk, M.A., Pentland, A.P.: Eigenfaces for recognition. J. Cogn. Neurosci. 13(1), 71–86 (1991)CrossRefGoogle Scholar
  48. 48.
    Turk, M.A., Pentland, A.P.: Face recognition using eigenfaces. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 586–591 (1991)Google Scholar
  49. 49.
    Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 511– 518 (2001)Google Scholar
  50. 50.
    Wang, L., He, D.C.: Texture classification using texture spectrum. IEEE Trans. Pattern Anal. Mach. Int. 23(8), 905–910 (1990)Google Scholar
  51. 51.
    WVHTCF: Tactical imager for night/day extended-range surveillance. (online) http://www.wvhtf.org/programs/advancedtech/ONR
  52. 52.
    Yao, Y., Abidi, B., Abidi, M.: Digital imaging with extreme zoom: System design and image restoration. In: Proceedings of the IEEE International Conference on Computer Vision Systems (2006)Google Scholar
  53. 53.
    Zhang, L., Chu, R., Xiang, S., Liao, S., Li, S.Z.: Face detection based on multi-block lbp representation. In: Advances in Biometrics, Lecture Notes in Computer Science, vol. 4642, pp. 11–18. Springer, Berlin, Heidelberg (2007)Google Scholar
  54. 54.
    Zhao, G., Pietikinen, M.: Dynamic texture recognition using local binary patterns with an application to facial expressions. IEEE Trans. Pattern Anal. Mach. Intell. 29(6), 915–928 (2007)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Lane Department of Computer Science and Electrical EngineeringWest Virginia UniversityMorgantownUSA

Personalised recommendations