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Real-time AdaBoost cascade face tracker based on likelihood map and optical flow

Real-time AdaBoost cascade face tracker based on likelihood map and optical flow

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The authors present a novel face tracking approach where optical flow information is incorporated into a modified version of the Viola–Jones detection algorithm. In the original algorithm, detection is static, as information from previous frames is not considered; in addition, candidate windows have to pass all stages of the classification cascade, otherwise they are discarded as containing no face. In contrast, the proposed tracker preserves information about the number of classification stages passed by each window. Such information is used to build a likelihood map, which represents the probability of having a face located at that position. Tracking capabilities are provided by extrapolating the position of the likelihood map to the next frame by optical flow computation. The proposed algorithm works in real time on a standard laptop. The system is verified on the Boston Head Tracking Database, showing that the proposed algorithm outperforms the standard Viola–Jones detector in terms of detection rate and stability of the output bounding box, as well as including the capability to deal with occlusions. The authors also evaluate two recently published face detectors based on convolutional networks and deformable part models with their algorithm showing a comparable accuracy at a fraction of the computation time.

References

    1. 1)
      • (2011)
        1. Li, S.Z., Jain, A.K. (Eds.): ‘Handbook of face recognition’ (Springer Verlag, 2011, 2nd edn.).
        .
    2. 2)
      • A.K. Jain , A. Kumar . (2010)
        2. Jain, A.K., Kumar, A.: ‘Biometrics of next generation: an overview’, in Mordini, E., Tzovaras, D. (Eds.): ‘Second generation biometrics’ (Springer, 2010).
        .
    3. 3)
      • J. Stallkamp , H.K. Ekenel , R. Stiefelhagen .
        3. Stallkamp, J., Ekenel, H.K., Stiefelhagen, R.: ‘Video-based face recognition on real-world data’. Proc. Int. Conf. Computer Vision, ICCV, October 2007, pp. 18.
        . Proc. Int. Conf. Computer Vision, ICCV , 1 - 8
    4. 4)
      • P. Viola , M. Jones .
        4. Viola, P., Jones, M.: ‘Rapid object detection using a boosted cascade of simple features’. Proc. Computer Vision and Pattern Recognition Conf., CVPR, December 2001, vol. 1, pp. 511518.
        . Proc. Computer Vision and Pattern Recognition Conf., CVPR , 511 - 518
    5. 5)
      • S.Z. Li . (2011)
        5. Li, S.Z.: ‘Face detection’, in Li, S.Z., Jain, A. (Eds.): ‘Handbook of face recognition’ (Springer Verlag, 2011, 2nd edn.).
        .
    6. 6)
      • A. Ranftl , F. Alonso-Fernandez , S. Karlsson .
        6. Ranftl, A., Alonso-Fernandez, F., Karlsson, S.: ‘Face tracking using optical flow’. Proc. Int. Conf. the Biometrics Special Interest Group, BIOSIG (Best Paper Award), September 2015, pp. 15.
        . Proc. Int. Conf. the Biometrics Special Interest Group, BIOSIG (Best Paper Award) , 1 - 5
    7. 7)
      • K. Zhang , Z. Zhang , Z. Li .
        7. Zhang, K., Zhang, Z., Li, Z., et al: ‘Joint face detection and alignment using multitask cascaded convolutional networks’, IEEE Signal Process. Lett., 2016, 23, (10), pp. 14991503.
        . IEEE Signal Process. Lett. , 10 , 1499 - 1503
    8. 8)
      • O.M. Parkhi , A. Vedaldi , A. Zisserman .
        8. Parkhi, O.M., Vedaldi, A., Zisserman, A.: ‘Deep face recognition’. Proc. British Machine Vision Conf., BMVC, 2015.
        . Proc. British Machine Vision Conf., BMVC
    9. 9)
      • M. Mathias , R. Benenson , M. Pedersoli .
        9. Mathias, M., Benenson, R., Pedersoli, M., et al: ‘Face detection without bells and whistles’. Proc. European Conf. Computer Vision, ECCV, 2014, pp. 720735.
        . Proc. European Conf. Computer Vision, ECCV , 720 - 735
    10. 10)
      • M.-H. Yang , D. Kriegman , N. Ahuja .
        10. Yang, M.-H., Kriegman, D., Ahuja, N.: ‘Detecting faces in images: a survey’, IEEE Trans. Pattern Anal. Mach. Intell., 2002, 24, (1), pp. 3458.
        . IEEE Trans. Pattern Anal. Mach. Intell. , 1 , 34 - 58
    11. 11)
      • H.A. Rowley , S. Baluja , T. Kanade .
        11. Rowley, H.A., Baluja, S., Kanade, T.: ‘Neural network-based face detection’, IEEE Trans. Pattern Anal. Mach. Intell., 1998, 20, (1), pp. 2338.
        . IEEE Trans. Pattern Anal. Mach. Intell. , 1 , 23 - 38
    12. 12)
      • K.-K. Sung , T. Poggio .
        12. Sung, K.-K., Poggio, T.: ‘Example-based learning for view-based human face detection’, IEEE Trans. Pattern Anal. Mach. Intell., 1998, 20, (1), pp. 3951.
        . IEEE Trans. Pattern Anal. Mach. Intell. , 1 , 39 - 51
    13. 13)
      • Y.M. Li , S.G. Gong , H. Liddell .
        13. Li, Y.M., Gong, S.G., Liddell, H.: ‘Support vector regression and classification based multi-view face detection and recognition’. Proc. Int. Conf. Automatic Face and Gesture Recognition, FG, 2000.
        . Proc. Int. Conf. Automatic Face and Gesture Recognition, FG
    14. 14)
      • P. Viola , M. Jones .
        14. Viola, P., Jones, M.: ‘Robust real-time face detection’, Int. J. Comput. Vis., 2004, 57, (2), pp. 137154.
        . Int. J. Comput. Vis. , 2 , 137 - 154
    15. 15)
      • D. Teferi , J. Bigun .
        15. Teferi, D., Bigun, J.: ‘Evaluation protocol for the dxm2vts database and performance comparison of face detection and face tracking on video’. Int. Conf. Pattern Recognition, ICPR, December 2008, pp. 14.
        . Int. Conf. Pattern Recognition, ICPR , 1 - 4
    16. 16)
      • R. Lienhart , A. Kuranov , V. Pisarevsky .
        16. Lienhart, R., Kuranov, A., Pisarevsky, V.: ‘Empirical analysis of detection cascades of boosted classifiers for rapid object detection’. Proc. DAGM 25th Pattern Recognition Symp., 2003.
        . Proc. DAGM 25th Pattern Recognition Symp.
    17. 17)
      • L. Zhang , R. Chu , S. Xiang .
        17. Zhang, L., Chu, R., Xiang, S., et al: ‘Face detection based on multi-block lbp representation’. Proc. Int. Conf. Biometrics, ICB, 2007.
        . Proc. Int. Conf. Biometrics, ICB
    18. 18)
      • H. Li , Z. Lin , X. Shen .
        18. Li, H., Lin, Z., Shen, X., et al: ‘A convolutional neural network cascade for face detection’. Proc. Int. Conf. Computer Vision and Pattern Recognition, CVPR, June 2015, pp. 53255334.
        . Proc. Int. Conf. Computer Vision and Pattern Recognition, CVPR , 5325 - 5334
    19. 19)
      • Z. Liu , P. Luo , X. Wang .
        19. Liu, Z., Luo, P., Wang, X., et al: ‘Deep learning face attributes in the wild’. Proc. Int. Conf. Computer Vision, ICCV, 2015, pp. 37303738.
        . Proc. Int. Conf. Computer Vision, ICCV , 3730 - 3738
    20. 20)
      • S. Yang , P. Luo , C.C. Loy .
        20. Yang, S., Luo, P., Loy, C.C., et al: ‘From facial parts responses to face detection: a deep learning approach’. Proc. Int. Conf. Computer Vision, ICCV, 2015, pp. 36763684.
        . Proc. Int. Conf. Computer Vision, ICCV , 3676 - 3684
    21. 21)
      • C. Zhang , Z. Zhang .
        21. Zhang, C., Zhang, Z.: ‘Improving multiview face detection with multi-task deep convolutional neural networks’. Proc. IEEE Winter Conf. Applications of Computer Vision, March 2014, pp. 10361041.
        . Proc. IEEE Winter Conf. Applications of Computer Vision , 1036 - 1041
    22. 22)
      • V. Jain , E.G. Learned-Miller .
        22. Jain, V., Learned-Miller, E.G.: ‘FDDB: a benchmark for face detection in unconstrained settings’. Technical Report UMCS-2010-009, University of Massachusetts, Amherst, 2010.
        .
    23. 23)
      • M. Köstinger , P. Wohlhart , P.M. Roth .
        23. Köstinger, M., Wohlhart, P., Roth, P.M., et al: ‘Annotated facial landmarks in the wild: a large-scale, real-world database for facial landmark localization’. Proc. Int. Conf. Computer Vision Workshops, ICCV, November 2011, pp. 21442151.
        . Proc. Int. Conf. Computer Vision Workshops, ICCV , 2144 - 2151
    24. 24)
      • J. Li , T. Wang , Y. Zhang .
        24. Li, J., Wang, T., Zhang, Y.: ‘Face detection using surf cascade’. Proc. Int. Conf. Computer Vision Workshops, ICCV, November 2011, pp. 21832190.
        . Proc. Int. Conf. Computer Vision Workshops, ICCV , 2183 - 2190
    25. 25)
      • H. Li , G. Hua , Z. Lin .
        25. Li, H., Hua, G., Lin, Z., et al: ‘Probabilistic elastic part model for unsupervised face detector adaptation’. Proc. Int. Conf. Computer Vision, ICCV, December 2013, pp. 793800.
        . Proc. Int. Conf. Computer Vision, ICCV , 793 - 800
    26. 26)
      • S. Liao , A.K. Jain , S.Z. Li .
        26. Liao, S., Jain, A.K., Li, S.Z.: ‘A fast and accurate unconstrained face detector’, IEEE Trans. Pattern Anal. Mach. Intell., 2016, 38, (2), pp. 211223.
        . IEEE Trans. Pattern Anal. Mach. Intell. , 2 , 211 - 223
    27. 27)
      • W. Zhao , R. Chellappa , P. Phillips .
        27. Zhao, W., Chellappa, R., Phillips, P., et al: ‘Face recognition: a literature survey’, ACM Comput. Surv., 2003, 35, (4), pp. 399458.
        . ACM Comput. Surv. , 4 , 399 - 458
    28. 28)
      • F. Dornaika , J. Ahlberg .
        28. Dornaika, F., Ahlberg, J.: ‘Fast and reliable active appearance model search for 3-d face tracking’, IEEE Trans. Syst. Man Cybern. B, Cybern., 2004, 34, (4), pp. 18381853.
        . IEEE Trans. Syst. Man Cybern. B, Cybern. , 4 , 1838 - 1853
    29. 29)
      • F. Dornaika , J. Ahlberg .
        29. Dornaika, F., Ahlberg, J.: ‘Fitting 3d face models for tracking and active appearance model training’, Image Vis. Comput., 2006, 24, (9), pp. 10101024.
        . Image Vis. Comput. , 9 , 1010 - 1024
    30. 30)
      • P. Maurel , A. McGonigal , R. Keriven .
        30. Maurel, P., McGonigal, A., Keriven, R., et al: ‘3d model fitting for facial expression analysis under uncontrolled imaging conditions’. Proc. Int. Conf. Pattern Recognition, ICPR, December 2008, pp. 14.
        . Proc. Int. Conf. Pattern Recognition, ICPR , 1 - 4
    31. 31)
      • A.-K. Roy-Chowdhury , Y. Xu . (2015)
        31. Roy-Chowdhury, A.-K., Xu, Y.: ‘Face tracking’, in Li, S.Z., Jain, A. (Eds.): ‘Encyclopedia of biometrics’ (Springer, 2015, 2nd edn.).
        .
    32. 32)
      • E.R. Gast .
        32. Gast, E.R.: ‘A framework for real-time face and facial feature tracking using optical flow pre-estimation and template tracking’. MS thesis, Leiden University, 2010.
        .
    33. 33)
      • D. DeCarlo , D. Metaxas .
        33. DeCarlo, D., Metaxas, D.: ‘Optical flow constraints on deformable models with applications to face tracking’, Int. J. Comput. Vis., 2000, 38, (2), pp. 99127.
        . Int. J. Comput. Vis. , 2 , 99 - 127
    34. 34)
      • P. Delmas , N. Eveno , M. Lievin .
        34. Delmas, P., Eveno, N., Lievin, M.: ‘Towards robust lip tracking’. Proc. Int. Conf. Pattern Recognition, ICPR, 2002, vol. 2, pp. 528531.
        . Proc. Int. Conf. Pattern Recognition, ICPR , 528 - 531
    35. 35)
      • Z. Wu , P.S. Aleksic , A.K. Katsaggelos .
        35. Wu, Z., Aleksic, P.S., Katsaggelos, A.K.: ‘Lip tracking for mpeg-4 facial animation’. Proc. IEEE Int. Conf. Multimodal Interfaces, 2002, pp. 293298.
        . Proc. IEEE Int. Conf. Multimodal Interfaces , 293 - 298
    36. 36)
      • J. Chen , B. Tiddeman .
        36. Chen, J., Tiddeman, B.: ‘Multi-cue facial feature detection and tracking’. Proc. Int. Conf. Image and Signal Processing, ICISP, 2008, pp. 356367.
        . Proc. Int. Conf. Image and Signal Processing, ICISP , 356 - 367
    37. 37)
      • Z. Kalal , K. Mikolajczyk , J. Matas .
        37. Kalal, Z., Mikolajczyk, K., Matas, J.: ‘Face-TLD: tracking-learning-detection applied to faces’. Proc. Int. Conf. Image Processing, ICIP, 2010, pp. 37893792.
        . Proc. Int. Conf. Image Processing, ICIP , 3789 - 3792
    38. 38)
      • D. Comaniciu , V. Ramesh , P. Meer .
        38. Comaniciu, D., Ramesh, V., Meer, P.: ‘Kernel-based object tracking’, IEEE Trans. Pattern Anal. Mach. Intell., 2003, 25, (5), pp. 564577.
        . IEEE Trans. Pattern Anal. Mach. Intell. , 5 , 564 - 577
    39. 39)
      • J. Tao , Y.-P. Tan .
        39. Tao, J., Tan, Y.-P.: ‘A unified probabilistic approach to face detection and tracking’. IEEE Int. Symp. Circuits and Systems, SCAS, May 2005, vol. 4, pp. 37973800.
        . IEEE Int. Symp. Circuits and Systems, SCAS , 3797 - 3800
    40. 40)
      • H.-P. Huang , C.-T. Lin .
        40. Huang, H.-P., Lin, C.-T.: ‘Multi-camshift for multi-view faces tracking and recognition’. Proc. IEEE Int. Conf. Robotics and Biomimetics, ROBIO, December 2006, pp. 13341339.
        . Proc. IEEE Int. Conf. Robotics and Biomimetics, ROBIO , 1334 - 1339
    41. 41)
      • Q.-A. Nguyen , A. Robles-Kelly , C. Shen .
        41. Nguyen, Q.-A., Robles-Kelly, A., Shen, C.: ‘Enhanced kernel-based tracking for monochromatic and thermographic video’. IEEE Int. Conf. Video and Signal Based Surveillance, AVSS, November 2006, pp. 2828.
        . IEEE Int. Conf. Video and Signal Based Surveillance, AVSS , 28 - 28
    42. 42)
      • P. Vadakkepat , P. Lim , L.C. De-Silva .
        42. Vadakkepat, P., Lim, P., De-Silva, L.C., et al: ‘Multimodal approach to human-face detection and tracking’, IEEE Trans. Ind. Electron., 2008, 55, (3), pp. 13851393.
        . IEEE Trans. Ind. Electron. , 3 , 1385 - 1393
    43. 43)
      • P.S. Hiremath , M. Hiremath , R. Mahesh .
        43. Hiremath, P.S., Hiremath, M., Mahesh, R.: ‘Face detection and tracking in video sequence using fuzzy geometric face model and mean shift’, Int. J. Adv. Trends Comput. Sci. Eng., 2013, 2, (1), pp. 4146.
        . Int. J. Adv. Trends Comput. Sci. Eng. , 1 , 41 - 46
    44. 44)
      • D. Grest , R. Koch .
        44. Grest, D., Koch, R.: ‘Realtime multi-camera person tracking for immersive environments’. Proc. IEEE Workshop on Multimedia Signal Processing, September 2004, pp. 387390.
        . Proc. IEEE Workshop on Multimedia Signal Processing , 387 - 390
    45. 45)
      • C. Su , L. Huang .
        45. Su, C., Huang, L.: ‘Spatio-temporal graphical-model-based multiple facial feature tracking’, EURASIP J. Appl. Signal Process., 2005, 2005, pp. 20912100.
        . EURASIP J. Appl. Signal Process. , 2091 - 2100
    46. 46)
      • T. Yun , L. Guan .
        46. Yun, T., Guan, L.: ‘Fiducial point tracking for facial expression using multiple particle filters with kernel correlation analysis’. Proc. IEEE Int. Conf. Image Processing, ICIP, September 2010, pp. 373376.
        . Proc. IEEE Int. Conf. Image Processing, ICIP , 373 - 376
    47. 47)
      • Y.N. Chae , J. Ha , H.S. Yang .
        47. Chae, Y.N., Ha, J., Yang, H.S.: ‘Development of an efficient face detection and tracking system for mobile devices’. Proc. IEEE Int. Conf. Virtual Systems and Multimedia, VSMM, October 2010, pp. 192196.
        . Proc. IEEE Int. Conf. Virtual Systems and Multimedia, VSMM , 192 - 196
    48. 48)
      • Z. Zhu , Q. Ji .
        48. Zhu, Z., Ji, Q.: ‘3d face pose tracking from an uncalibrated monocular camera’. Proc. Int. Conf. Pattern Recognition, ICPR, August 2004, vol. 4, pp. 400403.
        . Proc. Int. Conf. Pattern Recognition, ICPR , 400 - 403
    49. 49)
      • V. Girondel , A. Caplier , L. Bonnaud .
        49. Girondel, V., Caplier, A., Bonnaud, L.: ‘Real time tracking of multiple persons by Kalman filtering and face pursuit for multimedia applications’. Proc. IEEE Southwest Symp. Image Analysis and Interpretation, March 2004, pp. 201205.
        . Proc. IEEE Southwest Symp. Image Analysis and Interpretation , 201 - 205
    50. 50)
      • A. Destrero , F. Odone , A. Verri .
        50. Destrero, A., Odone, F., Verri, A.: ‘A system for face detection and tracking in unconstrained environments’. Proc. IEEE Conf. Advanced Video and Signal Based Surveillance, AVSS, September 2007, pp. 499504.
        . Proc. IEEE Conf. Advanced Video and Signal Based Surveillance, AVSS , 499 - 504
    51. 51)
      • X. Bing , Y. Wei , C. Chareonsak .
        51. Bing, X., Wei, Y., Chareonsak, C.: ‘Automatic focusing technique for face detection and face contour tracking’. IEEE Int. Workshop on Biomedical Circuits and Systems, December 2004, pp. S3/2-9S3/2-12.
        . IEEE Int. Workshop on Biomedical Circuits and Systems , S3/2 - S3/9
    52. 52)
      • B. Mohabbati , S. Kasaei .
        52. Mohabbati, B., Kasaei, S.: ‘Face localization and versatile tracking in wavelet domain’. Proc. IEEE Conf. Information and Communication Technologies, ICTTA, 2006, vol. 1, pp. 15521556.
        . Proc. IEEE Conf. Information and Communication Technologies, ICTTA , 1552 - 1556
    53. 53)
      • E. Loutas , C. Nikou , I. Pitas .
        53. Loutas, E., Nikou, C., Pitas, I.: ‘An information theoretic approach to joint probabilistic face detection and tracking’. Proc. Int. Conf. Image Processing, ICIP, 2002, vol. 1, pp. I505–I–508.
        . Proc. Int. Conf. Image Processing, ICIP , I - 505–I–508
    54. 54)
      • B. Wu , B.-G. Hu , Q. Ji .
        54. Wu, B., Hu, B.-G., Ji, Q.: ‘A coupled hidden markov random field model for simultaneous face clustering and tracking in videos’, Pattern Recognit., 2017, 64, pp. 361373.
        . Pattern Recognit. , 361 - 373
    55. 55)
      • R. Subban , S. Muthukumar , P. Pasupathi .
        55. Subban, R., Muthukumar, S., Pasupathi, P., et al: ‘Face tracking techniques in color images: a study and review’, Int. J. Eng. Res. Technol., 2013, 2, (12), pp. 24812487.
        . Int. J. Eng. Res. Technol. , 12 , 2481 - 2487
    56. 56)
      • R. Jagathishwaran , K.S. Ravichandran , P. Jayaraman .
        56. Jagathishwaran, R., Ravichandran, K.S., Jayaraman, P.: ‘A survey on face detection and tracking’, World Appl. Sci. J., 2014, 29, pp. 140145.
        . World Appl. Sci. J. , 140 - 145
    57. 57)
      • M. Kristan , R. Pflugfelder , A. Leonardis . (2015)
        57. Kristan, M., Pflugfelder, R., Leonardis, A., et al: ‘The visual object tracking VOT2014 challenge results’ (Springer International Publishing, Cham, 2015), pp. 191217.
        .
    58. 58)
      • Y. Wu , J. Lim , M.H. Yang .
        58. Wu, Y., Lim, J., Yang, M.H.: ‘Object tracking benchmark’, IEEE Trans. Pattern Anal. Mach. Intell., 2015, 37, (9), pp. 18341848.
        . IEEE Trans. Pattern Anal. Mach. Intell. , 9 , 1834 - 1848
    59. 59)
      • V.D. My , A. Zell .
        59. My, V.D., Zell, A.: ‘Real time face tracking and pose estimation using an adaptive correlation filter for human-robot interaction’. Proc. European Conf. Mobile Robots, September 2013, pp. 119124.
        . Proc. European Conf. Mobile Robots , 119 - 124
    60. 60)
      • L.B. Zhou , H. Wang , W. Mou .
        60. Zhou, L.B., Wang, H., Mou, W., et al: ‘Robust face detection and tracking under natural conditions’. Proc. Int. Conf. Robotics and Biomimetics, ROBIO, December 2013, pp. 934939.
        . Proc. Int. Conf. Robotics and Biomimetics, ROBIO , 934 - 939
    61. 61)
      • L.N. Gaxiola , V.H. Díaz-Ramírez , J.J. Tapia . (2014)
        61. Gaxiola, L.N., Díaz-Ramírez, V.H., Tapia, J.J., et al: ‘Robust face tracking with locally-adaptive correlation filtering’ (Springer International Publishing, Cham, 2014), pp. 925932.
        .
    62. 62)
      • H.K. Galoogahi , T. Sim , S. Lucey .
        62. Galoogahi, H.K., Sim, T., Lucey, S.: ‘Correlation filters with limited boundaries’. Proc. Conf. Computer Vision and Pattern Recognition, CVPR, June 2015, pp. 46304638.
        . Proc. Conf. Computer Vision and Pattern Recognition, CVPR , 4630 - 4638
    63. 63)
      • W. Chen , K. Zhang , Q. Liu .
        63. Chen, W., Zhang, K., Liu, Q.: ‘Robust visual tracking via patch based kernel correlation filters with adaptive multiple feature ensemble’, Neurocomputing, 2016, 214, pp. 607617.
        . Neurocomputing , 607 - 617
    64. 64)
      • J. Bigun . (2006)
        64. Bigun, J.: ‘Vision with direction’ (Springer, 2006).
        .
    65. 65)
      • J. Shi , C. Tomasi .
        65. Shi, J., Tomasi, C.: ‘Good features to track’. Proc. Int. Conf. Computer Vision and Pattern Recognition, CVPR, 1994.
        . Proc. Int. Conf. Computer Vision and Pattern Recognition, CVPR
    66. 66)
      • J. Bigun , G.H. Granlund .
        66. Bigun, J., Granlund, G.H.: ‘Optimal orientation detection of linear symmetry’. Proc. Int. Conf. Computer Vision, ICCV, June 1987, pp. 433438.
        . Proc. Int. Conf. Computer Vision, ICCV , 433 - 438
    67. 67)
      • C. Harris , M. Stephens .
        67. Harris, C., Stephens, M.: ‘A combined corner and edge detector’. Proc. Fourth Alvey Vision Conf., 1988, pp. 147151.
        . Proc. Fourth Alvey Vision Conf. , 147 - 151
    68. 68)
      • B.D. Lucas , T. Kanade .
        68. Lucas, B.D., Kanade, T.: ‘An iterative image registration technique with an application to stereo vision’. Proc. Int. Joint Conf. Artificial Intelligence, IJCAI, October 1981, pp. 674679.
        . Proc. Int. Joint Conf. Artificial Intelligence, IJCAI , 674 - 679
    69. 69)
      • S.M. Karlsson , J. Bigun .
        69. Karlsson, S.M., Bigun, J.: ‘Lip-motion events analysis and lip segmentation using optical flow’. Proc. IEEE Computer Vision and Pattern Recognition Biometrics Workshop, CVPRW, 2012.
        . Proc. IEEE Computer Vision and Pattern Recognition Biometrics Workshop, CVPRW
    70. 70)
      • G. Farneback .
        70. Farneback, G.: ‘Two-frame motion estimation based on polynomial expansion’. Proc. Swedish Symp. Image Analysis, SSBA, 2003.
        . Proc. Swedish Symp. Image Analysis, SSBA
    71. 71)
      • S. Yang , P. Luo , C.C. Loy .
        71. Yang, S., Luo, P., Loy, C.C., et al: ‘Wider face: a face detection benchmark’. Proc. Conf. Computer Vision and Pattern Recognition, CVPR, 2016.
        . Proc. Conf. Computer Vision and Pattern Recognition, CVPR
    72. 72)
      • J. Yan , X. Zhang , Z. Lei .
        72. Yan, J., Zhang, X., Lei, Z., et al: ‘Face detection by structural models’, Image Vis. Comput., 2014, 32, (10), pp. 790799, Best of Automatic Face and Gesture Recognition 2013.
        . Image Vis. Comput. , 10 , 790 - 799
    73. 73)
      • X. Zhu , D. Ramanan .
        73. Zhu, X., Ramanan, D.: ‘Face detection, pose estimation, and landmark localization in the wild’. Proc. Conf. Computer Vision and Pattern Recognition, CVPR, June 2012, pp. 28792886.
        . Proc. Conf. Computer Vision and Pattern Recognition, CVPR , 2879 - 2886
    74. 74)
      • M. La Cascia , S. Sclaroff , V. Athitsos .
        74. La Cascia, M., Sclaroff, S., Athitsos, V.: ‘Fast, reliable head tracking under varying illumination: an approach based on registration of texture-mapped 3d models’, IEEE Trans. Pattern Anal. Mach. Intell., 22, (4), 2000, pp. 322336.
        . IEEE Trans. Pattern Anal. Mach. Intell. , 322 - 336
    75. 75)
      • R. Valenti , T. Gevers .
        75. Valenti, R., Gevers, T.: ‘Robustifying eye center localization by head pose cues’. Proc. Int. Conf. Computer Vision and Pattern Recognition, CVPR, 2009.
        . Proc. Int. Conf. Computer Vision and Pattern Recognition, CVPR
    76. 76)
      • S.U. Jung , M.S. Nixon .
        76. Jung, S.U., Nixon, M.S.: ‘Model-based feature refinement by ellipsoidal face tracking’. Proc. Int. Conf. Pattern Recognition, ICPR, November 2012, pp. 12091212.
        . Proc. Int. Conf. Pattern Recognition, ICPR , 1209 - 1212
    77. 77)
      • C.N. Duong , T.C.P. Dinh , T.D. Ngo .
        77. Duong, C.N., Dinh, T.C.P., Ngo, T.D., et al: ‘Robust eye localization in video by combining eye detector and eye tracker’. Proc. Int. Conf. Pattern Recognition, ICPR, November 2012, pp. 242245.
        . Proc. Int. Conf. Pattern Recognition, ICPR , 242 - 245
    78. 78)
      • M. Irani .
        78. Irani, M.: ‘Multi-frame correspondence estimation using subspace constraints’, Int. J. Comput. Vis., 2002, 48, (3), pp. 173194.
        . Int. J. Comput. Vis. , 3 , 173 - 194
    79. 79)
      • Y. Taigman , M. Yang , M. Ranzato .
        79. Taigman, Y., Yang, M., Ranzato, M., et al: ‘Deepface: closing the gap to human-level performance in face verification’. Proc. Conf. Computer Vision and Pattern Recognition, CVPR, June 2014, pp. 17011708.
        . Proc. Conf. Computer Vision and Pattern Recognition, CVPR , 1701 - 1708
    80. 80)
      • P. Fischer , A. Dosovitskiy , E. Ilg .
        80. Fischer, P., Dosovitskiy, A., Ilg, E., et al: ‘Flownet: learning optical flow with convolutional networks’. Int. Conf. Computer Vision, ICCV, 2015.
        . Int. Conf. Computer Vision, ICCV
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