Journal of Signal Processing Systems

, Volume 82, Issue 3, pp 403–417 | Cite as

Robust Periocular Recognition by Fusing Sparse Representations of Color and Geometry Information

  • Juan C. Moreno
  • V. B. Surya Prasath
  • Gil Santos
  • Hugo Proença
Article

Abstract

In this paper, we propose a re-weighted elastic net (REN) model for biometric recognition. The new model is applied to data separated into geometric and color spatial components. The geometric information is extracted using a fast cartoon - texture decomposition model based on a dual formulation of the total variation norm allowing us to carry information about the overall geometry of images. Color components are defined using linear and nonlinear color spaces, namely the red-green-blue (RGB), chromaticity-brightness (CB) and hue-saturation-value (HSV). Next, according to a Bayesian fusion-scheme, sparse representations for classification purposes are obtained. The scheme is numerically solved using a gradient projection (GP) algorithm. In the empirical validation of the proposed model, we have chosen the periocular region, which is an emerging trait known for its robustness against low quality data. Our results were obtained in the publicly available FRGC and UBIRIS.v2 data sets and show consistent improvements in recognition effectiveness when compared to related state-of-the-art techniques.

Keywords

Sparse representation Periocular recognition Total variation Elastic net regularization Color Texture decomposition 

References

  1. 1.
    Adams, J., Woodard, D.L., Dozier, G., Miller, P., Bryant, K., & Glenn, G. (2010). Genetic-based type ii feature extraction for periocular biometric recognition: Less is more. In Pattern Recognition (ICPR), 2010 20th International Conference on (pp. 205–208).Google Scholar
  2. 2.
    Bharadwaj, S., Bhatt, H.S., Vatsa, M., & Singh, R. (2010). Periocular biometrics: When iris recognition fails. In IEEE International Conference on Biometrics: Theory Applications and Systems (BTAS) (pp. 1–6).Google Scholar
  3. 3.
    Bresson, X., Esedoglu, S., Vandergheynst, P., Thiran, J., & Osher, S. (2007). Fast global minimization of the active contour/snake model. Journal of Mathematical Imaging and Vision, 28(2), 151–167.CrossRefMathSciNetGoogle Scholar
  4. 4.
    Candès, E., Romberg, J., & Tao, T. (2006). Stable signal recovery from incomplete and inaccurate measurements. Communications on Pure and Applied Mathematics, 59(8), 1207–1223.CrossRefMathSciNetMATHGoogle Scholar
  5. 5.
    Candes, E., & Tao, T. (2005). Decoding by linear programming. IEEE Transactions on Information Theory, 51(12), 4203–4215.CrossRefMathSciNetMATHGoogle Scholar
  6. 6.
    Candes, E., & Tao, T. (2007). The Dantzig selector: statistical estimation when p is much larger than n. The Annals of Statistics, 35(6), 2392–2404.CrossRefMathSciNetGoogle Scholar
  7. 7.
    Candès, E., Wakin, M., & Boyd, S.P. (2008). Enhancing sparsity by reweighted 1− minimization. Journal of Fourier Analysis and Applications, 14(5), 877–905.CrossRefMathSciNetMATHGoogle Scholar
  8. 8.
    Chartrand, R., & Yin, W. (2008). Iteratively reweighted algorithms for compressive sensing. In IEEE International Conference on Acoustics, Speech, and Signal Processing (pp. 3869–3872).Google Scholar
  9. 9.
    Chen, S., Donoho, D., & Saunders, M. (1998). Atomic decomposition by basis pursuit. SIAM Journal on Scientific Computing, 20(1), 33–61.CrossRefMathSciNetGoogle Scholar
  10. 10.
    Dalal, N., & Triggs, B. (2005). Histograms of oriented gradients for human detection. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 886–893).Google Scholar
  11. 11.
    Daugman, J. (1993). High confidence visual recognition of persons by a test of statistical independence. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(11), 1148–1161.CrossRefGoogle Scholar
  12. 12.
    Donoho, D. (2006). For most large underdetermined systems of equations, the minimal 1-norm near-solution approximates the sparsest near-solution. Communications on Pure and Applied Mathematics, 59(7), 907–934.CrossRefMathSciNetGoogle Scholar
  13. 13.
    Fan, J., & Lv, J. (2008). Sure indepedence screening for ultrahigh dimensional feature space. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 70(5), 849–911.CrossRefMathSciNetGoogle Scholar
  14. 14.
    Fazel, M., Hindi, H., & Boyd, S. (2003). Log-det heuristic for matrix rank minimization with applications to Hankel and Euclidean distance matrices. In Proceedings of the American Control Conference (pp. 2156–2162).Google Scholar
  15. 15.
    Figueiredo, M., Nowak, R., & Wright, S. (2007). Gradient projection for sparse reconstruction: Application to compressed sensing and other inverse problem. IEEE Journal of Selected Topics in Signal Processing, 1(4), 586–597.CrossRefGoogle Scholar
  16. 16.
    Fuchs, J. J. (1999). Multipath time-delay detection and stimation. IEEE Transactions on Signal Processing, 47(1), 237–243.CrossRefGoogle Scholar
  17. 17.
    Hong, D., & Zhang, F. (2010). Weigthed elastic net model for mass spectrometry image processing. Mathematical Modelling of Natural Phenomena, 5(3), 115–133.CrossRefMathSciNetMATHGoogle Scholar
  18. 18.
    Jain, A.K., Flynn, P., & Ross, A. (Eds.) (2007). Handbook of biometrics. New York, USA: Springer-Verlag.Google Scholar
  19. 19.
    Jia, J., & Yu, B. (2010). On model salection consistency of the elastic net when pn. Statistica Sinica, 20, 595–611.MathSciNetMATHGoogle Scholar
  20. 20.
    Jiang, R., Crookes, D., & Lie, N. (2010). Face recognition in global harmonic subspace. IEEE Transactions on Information Forensics and Security, 5(3), 416–424.CrossRefGoogle Scholar
  21. 21.
    Juefei-Xu, F., Cha, M., Heyman, J.L., Venugopalan, S., Abiantun, R., & Savvides, M. (2010). Robust local binary pattern feature sets for periocular biometric identification. In IEEE International Conference on Biometrics: Theory Applications and Systems (pp. 1–8).Google Scholar
  22. 22.
    Juefei-Xu, F., Luu, K., Savvides, M., Bui, T.D., & Suen, C.Y. (2011). Investigating age invariant face recognition based on periocular biometrics. In International Joint Conference on Biometrics (pp. 1–7).Google Scholar
  23. 23.
    Kang, S., & March, R. (2007). Variational models for image colorization via chromaticity and brightness decomposition. IEEE Transactions on Image Processing, 16(9), 2251–2261.CrossRefMathSciNetGoogle Scholar
  24. 24.
    Lange, K. (2004). Optimization. Springer Text in Stadistic. New York: Springer.Google Scholar
  25. 25.
    Lowe, D.G. (2004). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2), 91–110.CrossRefGoogle Scholar
  26. 26.
    Miller, P.E., Rawls, A.W., Pundlik, S.J., & Woodard, D.L. (2010). Personal identification using periocular skin texture. In ACM Symposium on Applied Computing, SAC’10 (pp. 1496–1500).Google Scholar
  27. 27.
    Moreno, J.C. (2014). Texture image segmentation by weighted image gradient norm terms based on local histogram and active contours. In Di Giamberardino, P., Iacoviello, D., Jorge, R.N., & Tavares, J.M.R.S. (Eds.) Computational Modeling of Objects Presented in Images (pp. 225–243). New York: Springer.Google Scholar
  28. 28.
    Moreno, J.C., Prasath, V.B.S., & Proenca, H. (2013). Robust periocular recognition by fusing local to holistic sparse representations. In Sixth International Conference on Security of Information and Networks (pp. 160–164). Aksaray, Turkey: Proceedings ACM Digital Library.Google Scholar
  29. 29.
    Moreno, J.C., Prasath, V.B.S., Vorotnikov, D., & Proenca, H. (2013). Adaptive diffusion constrained total variation scheme with application to cartoon + texture + edge image decomposition. Technical Report 1354. Portugal: University of Coimbra.Google Scholar
  30. 30.
    Ojala, T., Pietikainen, M., & Harwood, D. (1994). Performance evaluation of texture measures with classification based on Kullback discrimination of distributions. In International Conference on Pattern Recognition (ICPR), (Vol. 1 pp. 582–585).Google Scholar
  31. 31.
    Ojala, T., Pietikainen, M., & Maenpaa, T. (2002). Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(7), 971–987.CrossRefGoogle Scholar
  32. 32.
    Oliva, A., & Torralba, A. (2001). Modeling the shape of the scene: A holistic representation of the spatial envelope. International Journal of Computer Vision, 42, 145–175.CrossRefMATHGoogle Scholar
  33. 33.
    Park, U., & Jain, A.K. (2010). Face matching and retrieval using soft biometrics. IEEE Transactions on Information Forensics and Security, 5(3), 406–415.CrossRefGoogle Scholar
  34. 34.
    Park, U., Jillela, R.R., Ross, A., & Jain, A.K. (2011). Periocular biometrics in the visible spectrum. IEEE Transactions on Information Forensics and Security, 6(1), 96–106.CrossRefGoogle Scholar
  35. 35.
    Park, U., Ross, A., & Jain, A.K. (2009). Periocular bimetrics in the visible spectrum: A feasibility study. In IEEE International Conference on Biometrics: Theory, Applications, and Systems (pp. 153–158).Google Scholar
  36. 36.
    Philips, P., Flynn, P., Scruggs, T., Bowyer, K., Chang, J., Hoffman, K., Marques, J., Min, J., & Worek, W. (2005). Overview on the face recognition gran challenge. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 947– 954).Google Scholar
  37. 37.
    Pillai, J.K., Patel, V.M., Chellappa, R., & Ratha, N.K. (2011). Secure and robust iris recognition using random projections and sparse representations. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(9), 1877–1893.CrossRefGoogle Scholar
  38. 38.
    Prasath, V.B.S., Palaniappan, K., & Seetharaman, G. (2012). Multichannel texture image segmentation using weighted feature fitting based variational active contours. In Eighth Indian Conference on Vision, Graphics and Image Processing (ICVGIP) (p. 6).Google Scholar
  39. 39.
    Proença, H., & Alexandre, L. (2012). Toward covert iris biometric recognition: experimental results from the NICE contests. IEEE Transactions on Information Forensics and Security, 7(2), 798–808.CrossRefGoogle Scholar
  40. 40.
    Proença, H., Filipe, S., Santos, R., Oliveira, J., & Alexandre, L.A. (2010). The UBIRIS.v2: A database of visible wavelength iris images captured on-the-move and at-a-distance. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(8), 1529–1535.CrossRefGoogle Scholar
  41. 41.
    Rudin, L., Osher, S., & Fatemi, E. (1992). Nonlinear total variation based noise removal algorithms. Physica D, 60(1–4), 259– 268.CrossRefMathSciNetMATHGoogle Scholar
  42. 42.
    Santos, G., & Proença, H. (2013). Periocular biometrics: An emerging technology for unconstrained scenarios. In IEEE Symposium on Computational Intelligence in Biometrics and Identity Management (CIBIM) (pp. 14–21).Google Scholar
  43. 43.
    Serafini, T., Zanghirati, G., & Zanni, L. (2003). Gradient projection methods for large quadratic programs and applications in training support vector machines. Optimization Methods and Software, 20(2-3), 353–378.CrossRefMathSciNetGoogle Scholar
  44. 44.
    Shekhar, S., Patel, V.M., Nasrabadi, N.M., & Chellappa, R. (2013). Joint sparse representation for robust multimodal biometrics recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(1), 113–126.CrossRefGoogle Scholar
  45. 45.
    Sznitman, R., & Jedynak, B. (2010). Active testing for face detection and localization. IEEE Transactions on Pattern Analysis and Machine Inteligence, 32(10), 1914–1920.CrossRefGoogle Scholar
  46. 46.
    Tang, B., Sapiro, G., & Caselles, V. (2001). Color image enhancement via chromaticity diffusion. IEEE Transactions on Image Processing, 10(5), 701–707.CrossRefMATHGoogle Scholar
  47. 47.
    Tibshirani, R. (1996). Regression shrinkage and selection via the LASSO. Journal of the Royal Statistical Society B, 58(1), 267–288.MathSciNetMATHGoogle Scholar
  48. 48.
    Wainwright, M. (2009). Sharp thresholds for high-dimensional and noisy sparsity recovery using 1−constrained quadratic programming (Lasso). IEEE Transactions on Information Theory, 55(5), 2183–2202.CrossRefMathSciNetGoogle Scholar
  49. 49.
    Wipf, D., & Nagarajan, S. (2010). Iterative reweighted 1 and 2 methods for finding sparse solutions. IEEE Journal of Selected Topics in Signal Processing, 4(2), 317–329.CrossRefGoogle Scholar
  50. 50.
    Woodard, D.L., Pundlik, S., Miller, P., Jillela, R., & Ross, A. (2010). On the fusion of periocular and iris biometrics in non-ideal imagery. In IEEE International Conference on Pattern Recognition (pp. 201–204). Istanbul, Turkey.Google Scholar
  51. 51.
    Wright, J., Yang, A.Y., Ganesh, A., Sastry, S., & Ma, Y. (2009). Robust face recognition via sparse representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(2), 210–227.CrossRefGoogle Scholar
  52. 52.
    Wyszecki, G., & Stiles, W. (1982). Color Science: Concepts and Methods, Quantitative Data and Formulas. New York: Wiley.Google Scholar
  53. 53.
    Zou, H. (2006). The adaptive lasso and its oracle properties. Journal of the American Statistical Association, 101(476), 1418–1429.CrossRefMathSciNetMATHGoogle Scholar
  54. 54.
    Zou, H., & Hastie, T. (2005). Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society: Series B, 67(2), 301–320.CrossRefMathSciNetMATHGoogle Scholar
  55. 55.
    Zou, H., & Zhang, H. (2009). On the adaptive elastic-net with a diverging number of parameters. The Annals of Statistics, 37(4), 1733–175.CrossRefMathSciNetMATHGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Juan C. Moreno
    • 1
  • V. B. Surya Prasath
    • 2
  • Gil Santos
    • 1
  • Hugo Proença
    • 1
  1. 1.IT-Instituto de Telecomunicações, Department of Computer ScienceUniversity of Beira InteriorCovilhaPortugal
  2. 2.Department of Computer ScienceUniversity of Missouri-ColumbiaMOUSA

Personalised recommendations