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access icon free Impact of eye detection error on face recognition performance

The locations of the eyes are the most commonly used features to perform face normalisation (i.e. alignment of facial features), which is an essential preprocessing stage of many face recognition systems. In this study, the authors study the sensitivity of open source implementations of five face recognition algorithms to misalignment caused by eye localisation errors. They investigate the ambiguity in the location of the eyes by comparing the difference between two independent manual eye annotations. They also study the error characteristics of automatic eye detectors present in two commercial face recognition systems. Furthermore, they explore the impact of using different eye detectors for training/enrolment and query phases of a face recognition system. These experiments provide an insight into the influence of eye localisation errors on the performance of face recognition systems and recommend a strategy for the design of training and test sets of a face recognition algorithm.


    1. 1)
      • 9. Wallace, R., McLaren, M., McCool, C., Marcel, S.: ‘Inter-session variability modelling and joint factor analysis for face authentication’. Int. Joint Conf. on Biometrics (IJCB), 2011, pp. 18.
    2. 2)
    3. 3)
      • 11. Marques, J., Orlans, N.M., Piszcz, A.T.: ‘Effects of eye position on eigenface-based face recognition scoring’, Image, 2000, 8.
    4. 4)
      • 8. Zhang, W., Shan, S., Gao, W., Chen, X., Zhang, H.: ‘Local Gabor binary pattern histogram sequence (LGBPHS): a novel non-statistical model for face representation and recognition’. IEEE Int. Conf. on Computer Vision (ICCV), 2005, vol. 1, pp. 786791.
    5. 5)
    6. 6)
      • 10. Shan, S., Chang, Y., Gao, W., Cao, B., Yang, P.: ‘Curse of mis-alignment in face recognition: problem and a novel mis-alignment learning solution’. IEEE Int. Conf. on Automatic Face and Gesture Recognition (FG), 2004, pp. 314320.
    7. 7)
      • 24. Neurotechnology Biometric SDK 4.2. Verilook 5.1. Software Development Kit (SDK), 2011.
    8. 8)
      • 12. Wang, P., Green, M.B., Ji, Q., Wayman, J.: ‘Automatic eye detection and its validation’. IEEE Computer Society Conf. on Computer Vision and Pattern Recognition – Workshops, 2005, CVPR Workshops, June 2005, pp. 164164.
    9. 9)
      • 23. Cognitec Systems GmbH. FaceVACS C + + SDK Version 8.4.0. Software Development Kit (SDK), 2010.
    10. 10)
    11. 11)
    12. 12)
    13. 13)
      • 7. Günther, M., Haufe, D., Würtz, R.P.: ‘Face recognition with disparity corrected Gabor phase differences’, in Villa, A.E.P., Duch, W., Érdi, P., Masulli, F., Palm, G. (Eds.): ‘Artificial neural networks and machine learning’ (Springer, Berlin, 2012), (LNCS7552), pp. 411418.
    14. 14)
      • 4. Wang, H., Flynn, P.J.: ‘Sensitivity of face recognition performance to eye location accuracy’. Biometric Technology for Human Identification II, Proc. SPIE 5779, 2005, pp. 122131.
    15. 15)
      • 18. Gross, R., Matthews, I., Cohn, J., Kanade, T., Baker, S.: ‘Multi-PIE’. IEEE Int. Conf. on Automatic Face Gesture Recognition (FG), 2008, pp. 18.
    16. 16)
    17. 17)
    18. 18)
    19. 19)
      • 16. Günther, M., Wallace, R., Marcel, S.: ‘An open source framework for standardized comparisons of face recognition algorithms’. Computer Vision – ECCV 2012. Workshops and Demonstrations, 2012, pp. 547556.
    20. 20)
      • 19. Jesorsky, O., Kirchberg, K.J., Frischholz, R.W.: ‘Robust face detection using the Hausdorff distance’. Audio-and Video-based Biometric Person Authentication, 2001, pp. 9095.
    21. 21)
      • 15. Ekenel, H.K., Stiefelhagen, R.: ‘Face alignment by minimizing the closest classification distance’. IEEE Third Int. Conf. on Biometrics: Theory, Applications, and Systems, 2009, BTAS'09, Septemebr 2009, pp. 16.
    22. 22)
      • 2. Min, J., Bowyer, K.W., Flynn, P.J.: ‘Eye perturbation approach for robust recognition of inaccurately aligned faces’, in Kanade, T., Jain, A.K., Ratha, N.K. (Eds.): ‘Audio- and video-based biometric person authentication’ (Springer, Berlin, Heidelberg, 2005),(LNCS3546), pp. 4150.
    23. 23)
    24. 24)
      • 17. Anjos, A., El Shafey, L., Wallace, R., Günther, M., McCool, C., Marcel, S.: ‘Bob: a free signal processing and machine learning toolbox for researchers’. 20th ACM Conf. on Multimedia Systems (ACMMM), Nara, Japan, October 2012, pp. 14491452.
    25. 25)
      • 1. Riopka, T., Boult, T.: ‘The eyes have it’. Proc. of the 2003 ACM SIGMM Workshop on Biometrics Methods and Applications, WBMA'03, New York, NY, USA, 2003, pp. 916.

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