A Multiclassifier-based Near-Real-Time Face Detection System

H. Wu and J. Zelek

References

  1. [1] L. Breiman, Bagging predictors, Machine Learning, 24(2),1996, 123–140.
  2. [2] J.L. Crowley & F. Berard, Multi-model tracking of faces forvideo communications, IEEE Proc. Int. Conf. on ComputerVision and Pattern Recognition, San Juan, Puerto Rico, June1997, 640–645. doi:10.1109/CVPR.1997.609393
  3. [3] S. Mckenna, S. Gong, & H. Liddell, Real-time tracking foran integrated face recognition system, Workshop on ParallelModelling of Neural Operators, Faro, Portugal, November1995, available at http://citeseer.ist.psu.edu/mckenna95realtime.html
  4. [4] C.H. Lee, J.S. Kim, & K.H. Park, Automatic human facelocation in a complex background, Pattern Recognition, 29,1996, 1877–1899. doi:10.1016/0031-3203(96)00036-2
  5. [5] I. Craw, H. Ellis, & J.R. Lishman, Automatic extraction offace-feature, Pattern Recognition Letters, 1987, 183–187. doi:10.1016/0167-8655(87)90039-0
  6. [6] P.J.L. Van Beek, M.J.T. Reinders, B. Sankur, & J.C.A. Van DerLubbe, Semantic segmentation of videophone image sequences,Proc. SPIE Int. Conf. on Visual Communications and ImageProcessing, Boston, MA, USA, vol. 1818, 1992, 1182–1193.
  7. [7] S.H. Jeng, H.Y. Liao, C.C. Han, M.Y. Chen, & Y.T. Liu, Facialfeature detection using geometrical face model: An efficientapproach, Pattern Recognition, 3(3), 1998, 273–282. doi:10.1016/S0031-3203(97)00048-4
  8. [8] M. Burl, T. Leung, & P. Perona, Face localization via shapestatistics, International Workshop on Face and Gesture Recognition, Zurich, Switzerland, June 1995, 154–159.
  9. [9] M. Turk & A. Pentland, Eigenfaces for recognition, Journal ofCognitive Neuroscience, 3, 1991, 71–86. doi:10.1162/jocn.1991.3.1.71
  10. [10] Y. Tian, T. Kanade, & J.F. Cohn, Dual-state parametric eyetracking, 4th IEEE Int. Conf. on Automatic Face and GestureRecognition, FG’00, Grenoble, France, March 2000, 110–115.
  11. [11] K. Lee, S. Lee, B. Lee, & G. Park, Automatic face detectionusing chromaticity space and deformable templates, Proc. Int.Conf. on Control, Automation and Systems, 2001.
  12. [12] F. Dornaika & J. Ahlberg, Fast and reliable active appearancemodel search for 3d face tracking, Proc. Mirage 2003, INRIA,Rocquencourt, France, March 2003, 113–122.
  13. [13] F. Perronnin, J. Dugelay, & K. Rose, Deformable face mappingfor person identification, Proc. ICIP, Barcelona, Spain, vol. 1,September 2003, 661–664.
  14. [14] A. Lanitis, A. Hill, T. Cootes, & C. Taylor, Locating facialfeatures using genetics algorithms, Proc. Int. Conf. on DigitalSignal Processing, Limassol, Cyrus, 1995, 520–525.
  15. [15] K.-K. Sung & T. Poggio, Example-based learning for view-based human face detection, IEEE Trans. on Pattern Analysisand Machine Intelligence, vol. 20, 1998, 39–51. doi:10.1109/34.655648
  16. [16] H.A. Rowley, S. Baluja, & T. Kanade, Neural network-basedface detection, IEEE Trans. on Pattern Analysis and MachineIntelligence, vol. 20, January 1998, 23–28. doi:10.1109/34.655647
  17. [17] A. Colmenarez & T. Huang, Face detection with information-based maximum discrimination, IEEE Proc. Int. Conf. onComputer Vision and Pattern Recognition, San Juan, PuertoRico, 1997, 782–787. doi:10.1109/CVPR.1997.609415
  18. [18] E. Osuna, R. Ferund, & F. Girosi, Training support vectormachines: An application to face detection, IEEE Proc. ofInt. Conf. on Computer Vision and Pattern Recognition, SanJuan, Puerto Rico, vol. 6, 1997, 130–136. doi:10.1109/CVPR.1997.609310
  19. [19] H. Schneiderman & T. Kanade, A statistical model for 3dobject detection applied to faces and cars, IEEE Conf. onComputer Vision and Pattern Recognition, Hilton Head Island,S.C., vol. 1, 2000, 746–751.
  20. [20] T. Rikert, M. Jones, & P. Viola, A cluster-based statisticalmodel for object detection, Proc. 7th IEEE Int. Conf. onComputer Vision, Corfu, Greece, vol. 2, 1999, 1046–1053.
  21. [21] R. Feraud, O.J. Bernier, J. Viallet, & M. Collobert, A fastand accurate face detector based on neural networks, IEEETrans. on Pattern Analysis and Machine Intelligence, 23(1),2001, 42–53. doi:10.1109/34.899945
  22. [22] P. Viola & M. Jones, Robust real-time object detection, 2nd Int.Workshop on Statistical and Computational Theories of Vision-Modelling, Learning, Computing, and Sampling, Vancouver,July 2001, available at http://www.stat.ucla.edu/∼sczhu/Workshops/SCTV2001.html
  23. [23] S.H. Kim, N.K. Kim, S.C. Ahn, & H.G. Kim, Object orientedface detection using range and color information, Proc. 3rdInt. Conf. on Automatic Face and Gesture Recognition, Nara,Japan, 1998, 76–81.
  24. [24] Q.B. Sun, W.M. Huang, & J.K. Wu, Face detection based oncolor and local symmetry information, Proc. 3rd Int. Conf. onAutomatic Face and Gesture Recognition, Nara, Japan, 1998,130–135. doi:10.1109/AFGR.1998.670937
  25. [25] Y. Dai & Y. Nakano, Face-texture model based sgld and itsapplication, Pattern Recognition, 29, 1996, 1007–1017. doi:10.1016/0031-3203(95)00139-5
  26. [26] E. Saber & A.M. Tekalp, Frontal-view face detection and facialfeature extraction using color, shape, and symmetry based costfunctions, Pattern Recognition Letters, October 1999, 20(10),1053–1068. doi:10.1016/S0167-8655(99)00072-0
  27. [27] M. Abdel-Mottaleb & A. Elgammal, Face detection in complexenvironments from color images, IEEE Proc. Int. Conf. onImage Processing, Kobe, Japan, vol. 3, October 1999, 622–626.
  28. [28] C. Garcia & G. Tziritas, Face detection using quantized skincolor regions, merging and wavelet packet analysis, IEEETrans. Multimedia, vol. 1, 1999, 264–277. doi:10.1109/6046.784465
  29. [29] J.C. Terrillon, H. Fukamachi, M.N. Shirazi, & S. Akamatsu,Comparative prefromance of different skin chrominance modelsand chrominance spaces for the automatic detection of humanfaces in color images, Proc. IEEE Int. Conf. on Face andGesture Recognition, Grenoble, France, 2000, 54–61. doi:10.1109/AFGR.2000.840612
  30. [30] J.C. Terrillon, M. David, & S. Akamatsu, Automatic detectionof human faces in natural scene images by use of a skin colormodel and of invariant moments, IEEE 3rd Int. Conf. onAutomatic Face and Gesture Recognition, Nara, Japan, 1998, 112–117. doi:10.1109/AFGR.1998.670934
  31. [31] S. McKenna, S. Gong, & Y. Raja, Modelling facial color andidentity with gaussian mixtures, Pattern Recognition, 31(12),1998, 1883–1892. doi:10.1016/S0031-3203(98)00066-1
  32. [32] B. Jedynak, H. Zheng, M. Daoudi, & D. Barret, Maximumentropy models for skin detection, Technical Report IRMA 57(XIII), Universite des Sciences et Technologies de Lille, France,2002.
  33. [33] R.C. Gonzalez & R.E. Woods, Digital image processing (NewYork: Addison Wesley, 1992), chap. 4.6.
  34. [34] D. Travis, Effective color displays: Theory and practice (SanDiego, CA: Academic Press, 1991).
  35. [35] D. Gabor, Theory of communication. Journal of the IEE, 93,1946, 429–459.
  36. [36] J.G. Daugman, Two-dimensional spectral analysis of corticalreceptive field profile, Vision Research, 20, 1980, 847–856. doi:10.1016/0042-6989(80)90065-6
  37. [37] J.G. Daugman, Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensionalvisual cortical filters, Journal of Optical Society of America,2(7), 1985, 160–169.
  38. [38] B. Schiele, Recognition without correspondence using multidimensional receptive field histograms, International Journal ofComputer Vision, 36(1), 2000, 31–52. doi:10.1023/A:1008120406972
  39. [39] Y. Freund & R.E. Shapire, A decision-theoretic generalizationof on-line learning and an application to boosting, Journal ofComputer and System Sciences, 55(1), 1995, 119–139. doi:10.1006/jcss.1997.1504
  40. [40] R. Schapire & Y. Singer, Improved boosting algorithms using confidence-rated predictions, Machine Learning, 37(3), 1999, 297-336 doi:10.1023/A:1007614523901

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