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Robust realtime face recognition and tracking system
Keywords:
meanshift, svm, wavelet, realtime face detection, realtime face tracking, face recognition, Kalman filterAbstract
There s some very important meaning in the study of realtime face recognition and tracking system for the video monitoring and artifical vision. The current method is still very susceptible to the illumination condition, non-real time and very common to fail to track the target face especially when partly covered or moving fast. In this paper, we propose to use Boosted Cascade combined with skin model for face detection and then in order to recognize the candidate faces, they will be analyzed by the hybrid Wavelet, PCA (principle component analysis) and SVM (support vector machine) method. After that, Meanshift and Kalman filter will be invoked to track the face. The experimental results show that the algorithm has quite good performance in terms of real-time and accuracy.
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References
[1] D. Comaniciu, V. Ramesh. 2000. Mean Shift and Optimal Prediction for Efficient Object Tracking, IEEE Int’l Conf. Image Processing, Vancouver, Canada, Vol. 3: 70-73.
[2] D. Comaniciu, V. Ramesh. 2000.Robust Detection and Tracking of Human Faces with an Active Camera, IEEE Int’l Workshop on Visual Surveillance, Dublin, Ireland, 11-18.
[3] D. Comaniciu, V. Ramesh, P. Meer. 2000. Real-Time Tracking of Non-Rigid Objects using Mean Shift, To appear, IEEE Conf. on Comp. Vis. and Pat. Rec., Hilton Head Island, South Carolina.
[4] Chien J T, Wu C C. 2002. Discriminant waveletfaces and nearest feature classifiers for face recognition [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(12): 1644-1649.
[5] Yang Minghsuan, Kriegman D J, Ahuja N. 2002. Detecting faces in images-a survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(1): 34-58.
[6] Viola P, Jones M. 2004. Robust Real-Time Face Detection[J] International Journal of ComputerVision 57(2): 137-154.
[7] Douglas Ngan King N. Face. 1999. Segmentation Using Skin-color Map in Videophone Applications[J]. IEEE Trans on CS, 9(4): 551-564.
[8] C.W.Hsu and C.J.Lin. 2002. A simple decomposition method for support vector machines.machine Learning, 46: 291-314.
[9] Huluta, E.; Petriu, E.M.; Das, S.R.; Al-Dhaher,A.H.;Instrumentation and Measurement Technology Conference, 2002. IMTC/2002. Proceedings of the 19th IEEE Volume 2, 21-23 May 2002 Page(s): 1537 - 1542 vol.2
[10] S.J.McKenna, Y.Raja, S.Gong. 1999. Tracking Colour Objects using Adaptive Mixture Model, Image and Vision Computing,17: 223-229.
[11] G. Dai and C. L. Zhou, “Face Recognition Using Support Vector Machines with the Robust Feature”, Proc. of the 2003 IEEE International Workshop on Robot and Human interactive Communication, 2003.
[12] Viola P, Michael J. Rapid Object Detection Using a Boosted Cascade of Simple Features[C]. Proc. of IEEE Conf. on Computer Vision and Pattern Recognition, Kauai, Hawaii, USA. 2001.
[13] Sirovich L., and Kirby M. 1987 . A low-dimensional procedure for the characterization of human faces, J.Opt. Soc. Amer. A, vol.4, no. 3, pp. 519-524..
[14] P. Belhumeur, J. Hespanha, D. Kriegman. 1997. Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence , 19(7):711–720
[15] M. Kirby, L. Sirovich. 1990. Application of the Karhunen-Loeve Procedure and the Characterization of Human Faces. IEEE Transactions on Pattern Analysis and Machine Intelligence , 12(1):103–108.
[16] R.T.Collins. 2003. Meanshift Blob Tracking through Scale Space. IEEE Conference on Computer Vision and Pattern Recognition, Vol.2, pp. 234-240.
[2] D. Comaniciu, V. Ramesh. 2000.Robust Detection and Tracking of Human Faces with an Active Camera, IEEE Int’l Workshop on Visual Surveillance, Dublin, Ireland, 11-18.
[3] D. Comaniciu, V. Ramesh, P. Meer. 2000. Real-Time Tracking of Non-Rigid Objects using Mean Shift, To appear, IEEE Conf. on Comp. Vis. and Pat. Rec., Hilton Head Island, South Carolina.
[4] Chien J T, Wu C C. 2002. Discriminant waveletfaces and nearest feature classifiers for face recognition [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(12): 1644-1649.
[5] Yang Minghsuan, Kriegman D J, Ahuja N. 2002. Detecting faces in images-a survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(1): 34-58.
[6] Viola P, Jones M. 2004. Robust Real-Time Face Detection[J] International Journal of ComputerVision 57(2): 137-154.
[7] Douglas Ngan King N. Face. 1999. Segmentation Using Skin-color Map in Videophone Applications[J]. IEEE Trans on CS, 9(4): 551-564.
[8] C.W.Hsu and C.J.Lin. 2002. A simple decomposition method for support vector machines.machine Learning, 46: 291-314.
[9] Huluta, E.; Petriu, E.M.; Das, S.R.; Al-Dhaher,A.H.;Instrumentation and Measurement Technology Conference, 2002. IMTC/2002. Proceedings of the 19th IEEE Volume 2, 21-23 May 2002 Page(s): 1537 - 1542 vol.2
[10] S.J.McKenna, Y.Raja, S.Gong. 1999. Tracking Colour Objects using Adaptive Mixture Model, Image and Vision Computing,17: 223-229.
[11] G. Dai and C. L. Zhou, “Face Recognition Using Support Vector Machines with the Robust Feature”, Proc. of the 2003 IEEE International Workshop on Robot and Human interactive Communication, 2003.
[12] Viola P, Michael J. Rapid Object Detection Using a Boosted Cascade of Simple Features[C]. Proc. of IEEE Conf. on Computer Vision and Pattern Recognition, Kauai, Hawaii, USA. 2001.
[13] Sirovich L., and Kirby M. 1987 . A low-dimensional procedure for the characterization of human faces, J.Opt. Soc. Amer. A, vol.4, no. 3, pp. 519-524..
[14] P. Belhumeur, J. Hespanha, D. Kriegman. 1997. Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence , 19(7):711–720
[15] M. Kirby, L. Sirovich. 1990. Application of the Karhunen-Loeve Procedure and the Characterization of Human Faces. IEEE Transactions on Pattern Analysis and Machine Intelligence , 12(1):103–108.
[16] R.T.Collins. 2003. Meanshift Blob Tracking through Scale Space. IEEE Conference on Computer Vision and Pattern Recognition, Vol.2, pp. 234-240.
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Published
2009-10-01
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[1]
“Robust realtime face recognition and tracking system”, JCS&T, vol. 9, no. 02, pp. p. 82–88, Oct. 2009, Accessed: Jan. 14, 2026. [Online]. Available: https://journal.info.unlp.edu.ar/JCST/article/view/721
