Machine Vision and Applications

, Volume 26, Issue 4, pp 519–531 | Cite as

Glasses detection on real images based on robust alignment

  • Alberto Fernández
  • Rodrigo García
  • Rubén Usamentiaga
  • Rubén Casado
Original Paper

Abstract

Automatic glasses detection on real face images is a challenging problem due to different appearance variations. Nevertheless, glasses detection on face images has not been thoroughly investigated. In this paper, an innovative algorithm for automatic glasses detection based on Robust Local Binary Pattern and robust alignment is proposed. Firstly, images are preprocessed and normalized in order to deal with scale and rotation. Secondly, eye glasses region is detected considering that the nosepiece of the glasses is usually placed at the same level as the center of the eyes in both height and width. Thirdly, Robust Local Binary Pattern is built to describe the eyes region, and finally, support vector machine is used to classify the LBP features. This algorithm can be applied as the first step of a glasses removal algorithm due to its robustness and speed. The proposed algorithm has been tested over the Labeled Faces in the Wild database showing a 98.65 % recognition rate. Influences of the resolution, the alignment of the normalized images and the number of divisions in the LBP operator are also investigated.

Keywords

Glasses detection Face alignment Robust Local Binary Pattern Face image processing 

References

  1. 1.
    Jiang, X., Binkert, M., Bernard, A., Bunke, H.: Towards detection of glasses in facial images. Pattern Anal. Appl. 3(1), 9–18 (2000)CrossRefGoogle Scholar
  2. 2.
    Dantcheva, A., Velardo, C., Dangelo, A., Dugelay, J.-L.: Bag of soft biometrics for person identification. Multimed. Tool. Appl. 51(2), 739–777 (2011)CrossRefGoogle Scholar
  3. 3.
    Jing, Z., Mariani, R.: Glasses detection and extraction by deformable contour. In: Proceedings 15th International Conference on Pattern Recognition, 2000, vol. 2, pp. 933–936. IEEE (2000)Google Scholar
  4. 4.
    Vaquero, D.A., Feris, R.S., Tran, D., Brown, L., Hampapur, A., Turk, M.: Attribute-based people search in surveillance environments. In: Workshop on Applications of Computer Vision (WACV), 2009, pp. 1–8. IEEE (2009)Google Scholar
  5. 5.
    Wu, B., Ai, H., Liu, R.: Glasses detection by boosting simple wavelet features. In: Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004, vol. 1, pp. 292–295. IEEE (2004)Google Scholar
  6. 6.
    Wu, H., Yoshikawa, G., Shioyama, T., Lao, T., Kawade, T.: Glasses frame detection with 3d hough transform. In: Proceedings 16th International Conference on Pattern Recognition, 2002, vol. 2, pp. 346–349. IEEE (20020Google Scholar
  7. 7.
    Shan, S., Gao, W., Lu, Y., Cao, B., Chen, X., Zhao, D., Zeng, W.: Unified framework for classifying facial images based on facial attribute-specific subspaces and minimum reconstruction error. In: Proc. of ACCV, vol. 2. Citeseer (2002)Google Scholar
  8. 8.
    Chenyu, W., Liu, C., Shum, H.-Y., Xy, Y.-Q., Zhang, Z.: Automatic eyeglasses removal from face images. IEEE Trans. Pattern Anal. Mach. Intell. 26(3), 322–336 (2004)CrossRefGoogle Scholar
  9. 9.
    Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2001, CVPR 2001, vol. 1, pp. I-511. IEEE (2001)Google Scholar
  10. 10.
    Heo, J., Kong, S.G., Abidi, B.R., Abidi, M.A.: Fusion of visual and thermal signatures with eyeglass removal for robust face recognition. In: Conference on Computer Vision and Pattern Recognition Workshop, 2004, CVPRW’04, pp. 122–122. IEEE (2004)Google Scholar
  11. 11.
    Jing, Z., Mariani, R., Wang, J.: Glasses detection for face recognition using bayes rules. In: Advances in Multimodal InterfacesICMI 2000, pp. 127–134. Springer, Berlin (2000)Google Scholar
  12. 12.
    Schapire, R.E., Singer, Y.: Improved boosting algorithms using confidence-rated predictions. Mach. Learn. 37(3), 297–336 (1999)CrossRefMATHGoogle Scholar
  13. 13.
    Xiao, Y., Yan, H.: Extraction of glasses in human face images. In: Biometric Authentication, pp. 214–220. Springer, Berlin (2004)Google Scholar
  14. 14.
    Park, J.-S., Oh, Y.H., Ahn, S.C., Lee, S.-W.: Glasses removal from facial image using recursive error compensation. IEEE Trans. Pattern Anal. Mach. Intell. 27(5), 805–811 (2005)CrossRefGoogle Scholar
  15. 15.
    Uricár, M., Franc, V., Hlavác, V.: Detector of facial landmarks learned by the structured output svm. In: VISAPP12: Proceedings of the 7th International Conference on Computer Vision Theory and Applications, vol. 1, pp. 547–556 (2012)Google Scholar
  16. 16.
    Wolf, L., Hassner, T., Taigman, Y.: Similarity scores based on background samples. In: Computer Vision-ACCV 2009, pp. 88–97. Springer, Berlin (2010)Google Scholar
  17. 17.
    Huang, G.B., Jain, V., Learned-Miller, E.: Unsupervised joint alignment of complex images. In: IEEE 11th International Conference on Computer Vision, 2007, ICCV 2007, pp. 1–8. IEEE (2007)Google Scholar
  18. 18.
    Huang, G.B., Mattar, M., Lee, H., Learned-Miller, E.G.: Learning to align from scratch. In: Neural Information Processing Systems. Neural Information Processing Systems (NIPS), Lake Tahoe (2012)Google Scholar
  19. 19.
    Makinen, E., Raisamo, R.: Evaluation of gender classification methods with automatically detected and aligned faces. IEEE Trans. Pattern Anal. Mach. Intell. 30(3), 541–547 (2008)CrossRefGoogle Scholar
  20. 20.
    Li, G., Cai, X., Li, X., Liu, Y.: An efficient face normalization algorithm based on eyes detection. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, 2006, pp. 3843–3848. IEEE (2006)Google Scholar
  21. 21.
    Mäkinen, E., Raisamo, R.: An experimental comparison of gender classification methods. Pattern Recogn. Lett. 29(10), 1544–1556 (2008)CrossRefGoogle Scholar
  22. 22.
    Wu, H., Yokoyama, T., Pramadihanto, D., Yachida, M.: Face and facial feature extraction from color image. In: Proceedings of the Second International Conference on Automatic Face and Gesture Recognition, 1996, pp. 345–350. IEEE (1996)Google Scholar
  23. 23.
    Wu, H., Inada, J., Shioyama, T., Chen, Q., Simada, T.: Automatic facial feature points detection with susan operator. In: Proceedings of the Scandinavian Conference on Image Analysis, pp. 257–263 (2001)Google Scholar
  24. 24.
    Lucey, P., Cohn, J.F., Kanade, T., Saragih, J., Ambadar, Z., Matthews, I.: The extended cohn-kanade dataset (ck\(+\)): a complete dataset for action unit and emotion-specified expression. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2010, pp. 94–101. IEEE (2010)Google Scholar
  25. 25.
    Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2, 27:1–27:27 (2011)CrossRefGoogle Scholar
  26. 26.
    Song, F., Tan, X., Chen, S., Zhou, Z.-H.: A literature survey on robust and efficient eye localization in real-life scenarios. Pattern Recogn. 46(12), 3157–3173 (2013)CrossRefGoogle Scholar
  27. 27.
    Huang, G.B., Ramesh, M., Berg, T., Learned-Miller, E.: Labeled faces in the wild: a database for studying face recognition in unconstrained environments. In: Technical Report 07–49. University of Massachusetts, Amherst (2007)Google Scholar
  28. 28.
    Ojala, T., Pietikäinen, M., Harwood, D.: A comparative study of texture measures with classification based on featured distributions. Pattern Recogn. 29(1), 51–59 (1996)CrossRefGoogle Scholar
  29. 29.
    Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)CrossRefGoogle Scholar
  30. 30.
    Guo, Z., Zhang, L., Zhang, D.: A completed modeling of local binary pattern operator for texture classification. IEEE Trans. Image Proc. 19(6), 1657–1663 (2010)CrossRefGoogle Scholar
  31. 31.
    Zhao, Y., Jia, W., Hu, R.-X., Min, H.: Completed robust local binary pattern for texture classification. Neurocomputing 106, 68–76 (2013)Google Scholar
  32. 32.
    Ahonen, T., Hadid, A., Pietikainen, M.: Face description with local binary patterns: Application to face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 28(12), 2037–2041 (2006)CrossRefGoogle Scholar
  33. 33.
    Shan, C.: Learning local binary patterns for gender classification on real-world face images. Pattern Recogn. Lett. 33(4), 431–437 (2012)CrossRefGoogle Scholar
  34. 34.
    Kumar, N., Berg, A.C., Belhumeur, P.N., Nayar, S.K.: Attribute and simile classifiers for face verification. In: IEEE 12th International Conference on Computer Vision, 2009, pp. 365–372. IEEE (2009)Google Scholar
  35. 35.
    Pietikäinen, M., Hadid, A., Zhao, G., Ahonen, T.: Local binary patterns for still images. In: Computer Vision Using Local Binary Patterns, pp. 13–47. Springer, Berlin (2011)Google Scholar
  36. 36.
    Huang, D., Shan, C., Ardabilian, M., Wang, Y., Chen, L.: Facial image analysis based on local binary patterns: a survey. IEEE Trans. Sys. Man Cyber.-Part C 41(6), 765–781 (2011)CrossRefGoogle Scholar
  37. 37.
    Ahonen, T., Hadid, A., Pietikäinen, M.: Face recognition with local binary patterns. In: Computer Vision-eccv 2004, pp. 469–481. Springer, Berlin (2004)Google Scholar
  38. 38.
    Zou, W.W., Yuen, P.C., Chellappa, R.: Low-resolution face tracker robust to illumination variations. IEEE Trans. Image Proc. 22(5), 1726–1739 (2013)CrossRefMathSciNetGoogle Scholar
  39. 39.
    Phillips, P.J., Moon, H., Rizvi, S.A., Rauss, P.J.: The feret evaluation methodology for face-recognition algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 22(10), 1090–1104 (2000)CrossRefGoogle Scholar
  40. 40.
    Huang, D., Shan, C., Ardabilian, M., Wang, Y., Chen, L.: Local binary patterns and its application to facial image analysis: a survey. IEEE Trans. Syst. Man Cyber. Part C Appl. Rev. 41(6), 765–781 (2011)Google Scholar
  41. 41.
    Song, F., Tan, X., Liu, X., Chen, S.: Eyes closeness detection from still images with multi-scale histograms of principal oriented gradients. Pattern Recogn. 47(9), 2825–2838 (2014)Google Scholar
  42. 42.
    Yuan, J.-H., Huang, D.-S., Zhu, H.-D., Gan, Y.: Completed hybrid local binary pattern for texture classification. In: International Joint Conference on Neural Networks (IJCNN), 2014, pp. 2050–2057. IEEE (2014)Google Scholar
  43. 43.
    Torralba, A., Efros, A.A.: Unbiased look at dataset bias. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011, pp. 1521–1528. IEEE (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Alberto Fernández
    • 1
  • Rodrigo García
    • 1
  • Rubén Usamentiaga
    • 2
  • Rubén Casado
    • 3
  1. 1.Fundación CTIC (Technological Center)Technological Scientific Park of GijónGijónSpain
  2. 2.Department of Computer Science and EngineeringUniversity of OviedoGijónSpain
  3. 3.Treelogic, Technological Scientific Park of AsturiasLlaneraSpain

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