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A Literature Survey on Eye Corner Detection Techniques in Real-Life Scenarios

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Advances in Computing and Data Sciences (ICACDS 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1046))

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Abstract

Accurate iris movement detection and tracking is an important and widely used step in many Human-computer interactive applications. Among the eye features, eye corners are considered as stable and reliable reference points to measure the relative iris motion. In real time scenarios, the presence of spectacles prohibit the current state-of-the-art methods to yield accurate detection as the appearance of eye corners changes considerably due to the glare and occlusion caused by them. We term this problem as the Spectacle problem. In this paper we review the available single and multiple image based spectacle problem removal techniques and highlight the pros and cons of the approaches. For this state-of-the-art report, we investigated research papers, patents and thesis presenting the basic definitions, terminologies and new directions for future researches.

This work was partially supported by the Board of Research in Nuclear Sciences (BRNS), Government of India, under the grant number 34/14/08/2016.

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References

  1. 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)

    Article  Google Scholar 

  2. Bengoechea, J.J., Cerrolaza, J.J., Villanueva, A., Cabeza, R.: Evaluation of accurate eye corner detection methods for gaze estimation. J. Eye Mov. Res. 7(3) (2014). https://bop.unibe.ch/JEMR/article/view/2381

  3. Xu, C., Zheng, Y., Wang, Z.: Semantic feature extraction for accurate eye corner detection. In: 2008 19th International Conference on Pattern Recognition, pp. 1–4, December 2008. https://doi.org/10.1109/ICPR.2008.4761409

  4. Erdogmus, N., Dugelay, J.L.: An efficient iris and eye corners extraction method. In: Hancock, E.R., Wilson, R.C., Windeatt, T., Ulusoy, I., Escolano, F. (eds.) SSPR/SPR 2010. LNCS, vol. 6218, pp. 549–558. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-14980-1_54

    Chapter  Google Scholar 

  5. Cheung, Y., Peng, Q.: Eye gaze tracking with a web camera in a desktop environment. IEEE Trans. Hum.-Mach. Syst. 45(4), 419–430 (2015). https://doi.org/10.1109/THMS.2015.2400442

    Article  Google Scholar 

  6. Yang, Y., Lu, Y.: A new method of the accurate eye corner location. In: Yao, J., et al. (eds.) RSCTC 2012. LNCS (LNAI), vol. 7413, pp. 116–125. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-32115-3_13

    Chapter  Google Scholar 

  7. Sirohey, S.A., Rosenfeld, A.: Eye detection in a face image using linear and nonlinear filters. Pattern Recogn. 34(7), 1367–1391 (2001). https://doi.org/10.1016/S0031-3203(00)00082-0. http://www.sciencedirect.com/science/article/pii/S0031320300000820

    Article  Google Scholar 

  8. Lam, K.M., Yan, H.: Locating and extracting the eye in human face images. Pattern Recogn. 29(5), 771–779 (1996). https://doi.org/10.1016/0031-3203(95)00119-0. http://www.sciencedirect.com/science/article/pii/0031320395001190

    Article  Google Scholar 

  9. Villanueva, A., Ponz, V., Sesma-Sanchez, L., Ariz, M., Porta, S., Cabeza, R.: Hybrid method based on topography for robust detection of iris center and eye corners. ACM Trans. Multimed. Comput. Commun. Appl. 9(4), 25:1–25:20 (2013). https://doi.org/10.1145/2501643.2501647

    Article  Google Scholar 

  10. Santos, G., Proença, H.: A robust eye-corner detection method for real-world data. In: 2011 International Joint Conference on Biometrics (IJCB), pp. 1–7, October 2011. https://doi.org/10.1109/IJCB.2011.6117596

  11. Herpers, R., Michaelis, M., Lichtenauer, K., Sommer, G.: Edge and keypoint detection in facial regions. In: Proceedings of the Second International Conference on Automatic Face and Gesture Recognition, pp. 212–217 (1996, Quarterly). https://doi.org/10.1109/AFGR.1996.557266

  12. Alkassar, S., Woo, W., Dlay, S., Chambers, J.: Efficient eye corner and gaze detection for sclera recognition under relaxed imaging constraints. In: 2016 24th European Signal Processing Conference (EUSIPCO), pp. 1965–1969. IEEE (2016)

    Google Scholar 

  13. Xu, G., Wang, Y., Li, J., Zhou, X.: Real time detection of eye corners and iris center from images acquired by usual camera. In: 2009 Second International Conference on Intelligent Networks and Intelligent Systems, pp. 401–404, November 2009. https://doi.org/10.1109/ICINIS.2009.109

  14. Jesorsky, O., Kirchberg, K.J., Frischholz, R.W.: Robust face detection using the hausdorff distance. In: Bigun, J., Smeraldi, F. (eds.) AVBPA 2001. LNCS, vol. 2091, pp. 90–95. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-45344-X_14

    Chapter  Google Scholar 

  15. Moriyama, T., Kanade, T., Xiao, J., Cohn, J.F.: Meticulously detailed eye region model and its application to analysis of facial images. IEEE Trans. Pattern Anal. Mach. Intell. 28(5), 738–752 (2006). https://doi.org/10.1109/TPAMI.2006.98

    Article  Google Scholar 

  16. Zhang, L.: Estimation of eye and mouth corner point positions in a knowledge-based coding system (1996). https://doi.org/10.1117/12.251289

  17. Pang, Z., Wei, C., Teng, D., Chen, D., Tan, H.: Robust eye center localization through face alignment and invariant isocentric patterns. PloS One 10(10), e0139098 (2015)

    Article  Google Scholar 

  18. Xia, H., Yan, G.: A novel method for eye corner detection based on weighted variance projection function. In: 2009 2nd International Congress on Image and Signal Processing, pp. 1–4, October 2009. https://doi.org/10.1109/CISP.2009.5304434

  19. Zhou, R., He, Q., Wu, J., Hu, C., Meng, Q.H.: Inner and outer eye corners detection for facial features extraction based on CTGF algorithm. In: Information Technology for Manufacturing Systems II. Applied Mechanics and Materials, vol. 58, pp. 1966–1971. Trans Tech Publications, July 2011. https://doi.org/10.4028/www.scientific.net/AMM.58-60.1966

    Article  Google Scholar 

  20. Valenti, R., Gevers, T.: Accurate eye center location through invariant isocentric patterns. IEEE Trans. Pattern Anal. Mach. Intell. 34(9), 1785–1798 (2012). https://doi.org/10.1109/TPAMI.2011.251

    Article  Google Scholar 

  21. Dibeklioglu, H., Salah, A.A., Gevers, T.: A statistical method for 2-D facial landmarking. IEEE Trans. Image Process. 21(2), 844–858 (2012). https://doi.org/10.1109/TIP.2011.2163162

    Article  MathSciNet  MATH  Google Scholar 

  22. Zhang, Z., Shen, Y., Lin, W., Zhou, B.: Eye corner detection with texture image fusion. In: 2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), pp. 992–995, December 2015. https://doi.org/10.1109/APSIPA.2015.7415420

  23. Kazemi, V., Sullivan, J.: One millisecond face alignment with an ensemble of regression trees. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1867–1874, June 2014. https://doi.org/10.1109/CVPR.2014.241

  24. Fernández, A., García, R., Usamentiaga, R., Casado, R.: Glasses detection on real images based on robust alignment. Mach. Vis. Appl. 26(4), 519–531 (2015). https://doi.org/10.1007/s00138-015-0674-1

    Article  Google Scholar 

  25. Jing, Z., Mariani, R., Wang, J.: Glasses detection for face recognition using bayes rules. In: Tan, T., Shi, Y., Gao, W. (eds.) ICMI 2000. LNCS, vol. 1948, pp. 127–134. Springer, Heidelberg (2000). https://doi.org/10.1007/3-540-40063-X_17

    Chapter  Google Scholar 

  26. Jia, X., Guo, J.: Eyeglasses removal from facial image based on phase congruency. In: 2010 3rd International Congress on Image and Signal Processing, vol. 4, pp. 1859–1862, October 2010. https://doi.org/10.1109/CISP.2010.5647366

  27. 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). https://doi.org/10.1109/TPAMI.2005.103

    Article  Google Scholar 

  28. Tian, Y., Kanade, T., Cohn, J.F.: Dual-state parametric eye tracking. In: Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580), pp. 110–115, March 2000. https://doi.org/10.1109/AFGR.2000.840620

  29. Zhu, Z., Ji, Q.: Robust real-time eye detection and tracking under variable lighting conditions and various face orientations. Comput. Vis. Image Underst. 98(1), 124–154 (2005). https://doi.org/10.1016/j.cviu.2004.07.012. http://www.sciencedirect.com/science/article/pii/S1077314204001158, Special Issue on Eye Detection and Tracking

    Article  Google Scholar 

  30. Wu, C., 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). https://doi.org/10.1109/TPAMI.2004.1262319

    Article  Google Scholar 

  31. Du, C., Su, G.: Eyeglasses removal from facial images. Pattern Recogn. Lett. 26(14), 2215–2220 (2005). https://doi.org/10.1016/j.patrec.2005.04.002. http://www.sciencedirect.com/science/article/pii/S0167865505001133

    Article  Google Scholar 

  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). https://doi.org/10.1109/TPAMI.2007.1014

    Article  Google Scholar 

  33. Xiao, Y., Yan, H.: Extraction of glasses in human face images. In: Zhang, D., Jain, A.K. (eds.) ICBA 2004. LNCS, vol. 3072, pp. 214–220. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-25948-0_30

    Chapter  Google Scholar 

  34. Liu, D., Shen, L., Yin, Y., Li, X.: How to recognize facial images with spectacles. In: 2006 6th World Congress on Intelligent Control and Automation, vol. 2, pp. 10153–10156 (2006). https://doi.org/10.1109/WCICA.2006.1713987

  35. Wang, Y., Jang, J., Tsai, L., Fan, K.: Improvement of face recognition by eyeglass removal. In: 2010 Sixth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, pp. 228–231, October 2010. https://doi.org/10.1109/IIHMSP.2010.64

  36. Yi, D., Li, S.Z.: Learning sparse feature for eyeglasses problem in face recognition. In: Face and Gesture 2011, pp. 430–435 (2011)

    Google Scholar 

  37. Liu, L., Sun, Y., Yin, B., Song, C.: Local gabor binary pattern random subspace method for eyeglasses-face recognition. In: 2010 3rd International Congress on Image and Signal Processing, vol. 4, pp. 1892–1896, October 2010. https://doi.org/10.1109/CISP.2010.5647554

  38. Burgos-Artizzu, X.P., Perona, P., Dollár, P.: Robust face landmark estimation under occlusion. In: 2013 IEEE International Conference on Computer Vision, pp. 1513–1520, December 2013. https://doi.org/10.1109/ICCV.2013.191

  39. Lazarus, M.Z., Gupta, S.: A low rank model based improved eye detection under spectacles. In: 2016 IEEE 7th Annual Ubiquitous Computing, Electronics Mobile Communication Conference (UEMCON), pp. 1–6, October 2016. https://doi.org/10.1109/UEMCON.2016.7777820

  40. Mayaluri, Z.L., Gupta, S.: Spectacle problem removal from facial images based on detail preserving filtering schemes. J. Intell. Fuzzy Syst. (Preprint) 36, 1–11 (2019)

    Article  Google Scholar 

  41. Burgos-Artizzu, X.P., Zepeda, J., Clerc, F.L., Pérez, P.: Pose and expression-coherent face recovery in the wild. In: 2015 IEEE International Conference on Computer Vision Workshop (ICCVW), pp. 877–885, December 2015. https://doi.org/10.1109/ICCVW.2015.117

  42. Gao, W., et al.: The cas-peal large-scale chinese face database and baseline evaluations. IEEE Trans. Syst. Man Cybern. - Part A: Syst. Hum. 38(1), 149–161 (2008). https://doi.org/10.1109/TSMCA.2007.909557

    Article  Google Scholar 

  43. Li, S.Z., Yi, D., Lei, Z., Liao, S.: The CASIA NIR-VIS 2.0 face database. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 348–353, June 2013. https://doi.org/10.1109/CVPRW.2013.59

  44. Moghaddam, B., Pentland, A.P.: Face recognition using view-based and modular eigenspaces (1994). https://doi.org/10.1117/12.191877

  45. Samaria, F.S., Harter, A.C.: Parameterisation of a stochastic model for human face identification. In: Proceedings of 1994 IEEE Workshop on Applications of Computer Vision, pp. 138–142, December 1994. https://doi.org/10.1109/ACV.1994.341300

  46. Martinez, A.M.: The AR face database. CVC Technical Report24 (1998)

    Google Scholar 

  47. Li, B.Y.L., Mian, A.S., Liu, W., Krishna, A.: Using kinect for face recognition under varying poses, expressions, illumination and disguise. In: 2013 IEEE Workshop on Applications of Computer Vision (WACV), pp. 186–192, January 2013. https://doi.org/10.1109/WACV.2013.6475017

  48. Thomaz, C.E.: Fei face database (2012). http://fei.edu.br/~cet/facedatabase.html. Accessed 13 Mar 2018

  49. Sagonas, C., Antonakos, E., Tzimiropoulos, G., Zafeiriou, S., Pantic, M.: 300 faces in-the-wild challenge: database and results. Image Vis. Comput. 47, 3–18 (2016). https://doi.org/10.1016/j.imavis.2016.01.002. http://www.sciencedirect.com/science/article/pii/S0262885616000147, 300-W, the First Automatic Facial Landmark Detection in-the-Wild Challenge

    Article  Google Scholar 

  50. Messer, K., Matas, J., Kittler, J., Jonsson, K.: XM2VTSDB: the extended M2VTS database. In: Second International Conference on Audio and Video-Based Biometric Person Authentication, pp. 72–77 (1999)

    Google Scholar 

  51. Hwang, B.W., Roh, M.C., Lee, S.W.: Performance evaluation of face recognition algorithms on Asian face database. In: Proceedings of the Sixth IEEE International Conference on Automatic Face and Gesture Recognition, pp. 278–283, May 2004. https://doi.org/10.1109/AFGR.2004.1301544

  52. Selinger, A., Socolinsky, D.A.: Appearance-based facial recognition using visible and thermal imagery: a comparative study. Technical report (2001)

    Google Scholar 

  53. Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Trans. Pattern Ana. Mach. Intell. 19(7), 711–720 (1997). https://doi.org/10.1109/34.598228

    Article  Google Scholar 

  54. Yi, D., Lei, Z., Liao, S., Li, S.Z.: Learning face representation from scratch. CoRR abs/1411.7923 (2014). http://arxiv.org/abs/1411.7923

  55. Huang, G.B., Ramesh, M., Berg, T., Learned-Miller, E.: Labeled faces in the wild: a database for studying face recognition in unconstrained environments. Technical report 07–49, University of Massachusetts, Amherst, October 2007

    Google Scholar 

  56. Phillips, P., Wechsler, H., Huang, J., Rauss, P.J.: The feret database and evaluation procedure for face-recognition algorithms. Image Vis. Comput. 16(5), 295–306 (1998). https://doi.org/10.1016/S0262-8856(97)00070-X

    Article  Google Scholar 

  57. Lazarus, M.Z., Gupta, S., Panda, N.: An Indian facial database highlighting the spectacle problems. In: IEEE International Conference on Innovative Technologies in Engineering 2018 (ICITE OU), April 2018. http://hdl.handle.net/2080/2992

  58. Tan, P., Quan, L., Lin, S.: Separation of highlight reflections on textured surfaces. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2006), vol. 2, pp. 1855–1860 (2006). https://doi.org/10.1109/CVPR.2006.273

  59. Liang, A., Pathirage, C.S.N., Wang, C., Liu, W., Li, L., Duan, J.: Face recognition despite wearing glasses. In: 2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA), pp. 1–8, November 2015. https://doi.org/10.1109/DICTA.2015.7371260

  60. Sandhan, T., Choi, J.Y.: Anti-glare: tightly constrained optimization for eyeglass reflection removal. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1675–1684, July 2017. https://doi.org/10.1109/CVPR.2017.182

  61. Levin, A., Weiss, Y.: User assisted separation of reflections from a single image using a sparsity prior. IEEE Trans. Pattern Anal. Mach. Intell. 29(9), 1647–1654 (2007). https://doi.org/10.1109/TPAMI.2007.1106

    Article  Google Scholar 

  62. Zhao, F., Feng, J., Zhao, J., Yang, W., Yan, S.: Robust lstm-autoencoders for face de-occlusion in the wild. IEEE Trans. Image Process. 27(2), 778–790 (2018). https://doi.org/10.1109/TIP.2017.2771408

    Article  MathSciNet  MATH  Google Scholar 

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Authors and Affiliations

  1. Department of Electrical Engineering, National Institute of Technology, Rourkela, 769008, Odisha, India

    M. Zefree Lazarus, Supratim Gupta & Nidhi Panda

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  1. M. Zefree Lazarus
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  2. Supratim Gupta
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  3. Nidhi Panda
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Correspondence to M. Zefree Lazarus .

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Editors and Affiliations

  1. University of KwaZulu-Natal, Durban, South Africa

    Mayank Singh

  2. Computer Science and Engineering, Jaypee Institute of Information Technology, Waknaghat, Himachal Pradesh, India

    P.K. Gupta

  3. Department of Computer Science and Engineering, Jaypee University of Engineering and Technology, Guna, Madhya Pradesh, India

    Vipin Tyagi

  4. ÚTIA AV ČR, Institute of Information Theory and Automation, Prague 8, Praha, Czech Republic

    Jan Flusser

  5. School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON, Canada

    Tuncer Ören

  6. CSE Department, Inderprastha Engineering College, Ghaziabad, Uttar Pradesh, India

    Rekha Kashyap

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Lazarus, M.Z., Gupta, S., Panda, N. (2019). A Literature Survey on Eye Corner Detection Techniques in Real-Life Scenarios. In: Singh, M., Gupta, P., Tyagi, V., Flusser, J., Ören, T., Kashyap, R. (eds) Advances in Computing and Data Sciences. ICACDS 2019. Communications in Computer and Information Science, vol 1046. Springer, Singapore. https://doi.org/10.1007/978-981-13-9942-8_56

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