Fast Facial Feature Extraction and Matching with Artificial Face Models
Facial features are frequently used to represent local
properties of a human face image in computer vision applications. In
this paper, we present a fast algorithm that can extract the facial
features online such that they can give a satisfying representation of a
face image. It includes one step for a coarse detection of each facial
feature by AdaBoost and another one to increase the accuracy of the
found points by Active Shape Models (ASM) in the regions of interest.
The resulted facial features are evaluated by matching with artificial
face models in the applications of physiognomy. The distance measure
between the features and those in the fate models from the database is
carried out by means of the Hausdorff distance. In the experiment, the
proposed method shows the efficient performance in facial feature
extractions and online system of physiognomy.
[1] G. P. Campadelli and R. Lanzarotti, "Fiducial point localization in color
images of face foregrounds," Image and Vision Computing, vol. 22, pp.
863-872, 2004.
[2] J. Matas, P. B'─▒lek, M. Hamouz, and J. Kittler, "Discriminative regions for
human face detection," in in Proceedings of Asian Conference on
Computer Vision, 2002.
[3] M. Hamouz, J. Kittler, J.-K. Kamarainen, P. Paalanen, H. Kalviainen, and
J. Matas, "Feature-based affine-invariant localization of faces," IEEE
Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no.
9, pp. 1490-1495, 2005.
[4] T. Kim, H. Kim, W. Hwang, S. Kee, and J. Kittler, "Component-based
LDA face descriptor for image retrieval," in The 13th British Machine
Vision Conference, 2002.
[5] H. Jee, K. Lee, and S. Pan, "Eye and face detection using svm," in
Intelligent Sensors, Sensor Networks and Information Processing
Conference, pp. 577-580, 2004.
[6] Z. Zhu and Q. Ji, "Robust real-time eye detection and tracking under
variable lighting conditions and various face orientations," Comput. Vis.
Image Underst., vol. 98, no. 1, pp. 124-154, 2005.
[7] M. H. Nguyen, J. Perez, and F. D. la Torre Frade, "Facial feature detection
with optimal pixel reduction svms," in 8th IEEE International Conference
on Automatic Face and Gesture Recognition, September 2008.
[8] Y.-S. Ryu and S.-Y. Oh, "Automatic extraction of eye and mouth fields
from a face image using eigenfeatures and ensemble networks," Applied
Intelligence, vol. 17, no. 2, pp. 171-185, 2002.
[9] S. Duffner and C. Garcia, "A connexionist approach for robust and
precise facial feature detection in complex scenes," in Proceedings of the
4th International Symposium on Image and Signal Processing and
Analysis, pp. 316-321, Sept. 2005.
[10] P. Viola, M. J. Jonse, "Robust Real-Time Face Detection," International
Journal of Computer Vision, vol. 57, no. 2, pp. 137-154, 2004.
[11] S. Milborrow and F. Nicolls, "Locating facial features with an extended
active shape model," in ECCV 08, pp. IV: 504-513, 2008.
[12] Z.-L. Zheng and F. Yang, "Enhanced active shape model for facial feature
localization," in 2008 International Conference on Machine Learning
and Cybernetics, vol. 5, pp. 2841-2845, July 2008.
[13] M. Haj, J. Orozco, J. Gonzalez, and J. Villanueva, "Automatic face and
facial features initialization for robust and accurate tracking," in
International Conference on Pattern Recognition, pp. 1-4, 2008.
[14] Y. Freund and R. E. Schapire, "Experiments with a New Boosting
Aalgorithm," in 13th International Conference on Machine Learning, pp.
148-156, 1996.
[15] R. E. Schapire and Y. Singer, "Improved Boosting Algorithms Using
Confidence-rated Predictions." Machine Learning, vol. 37, no. 3, pp.
297-336, 1999.
[16] J. Friedman, T. Hastie, and R. Tibshirani, "Additive Logistic Regression:
a Statistical View of Boosting," The Annals of Statistics, vol.28, no.2,
pp.337-407, 2000.
[17] L. L. Huang, A. Shimizu, "A multi-expert approach for robust face
detection," Pattern Recognition, vol. 39, pp. 1695-1703, 2006.
[18] R. L. Hsu, M. A. Mottaleb, A.K. Jain, Face detection in color images,
IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 5, pp. 695- 705,
2002.
[19] A. Cheddad, J. Condell, K. Curran, P. Kevitt, "A skin tone detection
algorithm for an adaptive approach to steganography," Singal Processing,
vol.89, pp. 2465-2478, 2009.
[20] R.C. Gonzalez and R.E. Woods, Digital image processing,
Addison-Wesley Publishing Company, Inc., 1992.
[21] D.P. Huttenlocher, G.A. Klanderman, W.J. Rucklidge, Comparing images
using the Hausdorff distance, IEEE Transactions on Pattern Analysis and
Machine Intelligence, vol. 15, no. 9, pp. 850-863, 1993.
[22] http://www.y28predictions.com/program/index.php
[1] G. P. Campadelli and R. Lanzarotti, "Fiducial point localization in color
images of face foregrounds," Image and Vision Computing, vol. 22, pp.
863-872, 2004.
[2] J. Matas, P. B'─▒lek, M. Hamouz, and J. Kittler, "Discriminative regions for
human face detection," in in Proceedings of Asian Conference on
Computer Vision, 2002.
[3] M. Hamouz, J. Kittler, J.-K. Kamarainen, P. Paalanen, H. Kalviainen, and
J. Matas, "Feature-based affine-invariant localization of faces," IEEE
Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no.
9, pp. 1490-1495, 2005.
[4] T. Kim, H. Kim, W. Hwang, S. Kee, and J. Kittler, "Component-based
LDA face descriptor for image retrieval," in The 13th British Machine
Vision Conference, 2002.
[5] H. Jee, K. Lee, and S. Pan, "Eye and face detection using svm," in
Intelligent Sensors, Sensor Networks and Information Processing
Conference, pp. 577-580, 2004.
[6] Z. Zhu and Q. Ji, "Robust real-time eye detection and tracking under
variable lighting conditions and various face orientations," Comput. Vis.
Image Underst., vol. 98, no. 1, pp. 124-154, 2005.
[7] M. H. Nguyen, J. Perez, and F. D. la Torre Frade, "Facial feature detection
with optimal pixel reduction svms," in 8th IEEE International Conference
on Automatic Face and Gesture Recognition, September 2008.
[8] Y.-S. Ryu and S.-Y. Oh, "Automatic extraction of eye and mouth fields
from a face image using eigenfeatures and ensemble networks," Applied
Intelligence, vol. 17, no. 2, pp. 171-185, 2002.
[9] S. Duffner and C. Garcia, "A connexionist approach for robust and
precise facial feature detection in complex scenes," in Proceedings of the
4th International Symposium on Image and Signal Processing and
Analysis, pp. 316-321, Sept. 2005.
[10] P. Viola, M. J. Jonse, "Robust Real-Time Face Detection," International
Journal of Computer Vision, vol. 57, no. 2, pp. 137-154, 2004.
[11] S. Milborrow and F. Nicolls, "Locating facial features with an extended
active shape model," in ECCV 08, pp. IV: 504-513, 2008.
[12] Z.-L. Zheng and F. Yang, "Enhanced active shape model for facial feature
localization," in 2008 International Conference on Machine Learning
and Cybernetics, vol. 5, pp. 2841-2845, July 2008.
[13] M. Haj, J. Orozco, J. Gonzalez, and J. Villanueva, "Automatic face and
facial features initialization for robust and accurate tracking," in
International Conference on Pattern Recognition, pp. 1-4, 2008.
[14] Y. Freund and R. E. Schapire, "Experiments with a New Boosting
Aalgorithm," in 13th International Conference on Machine Learning, pp.
148-156, 1996.
[15] R. E. Schapire and Y. Singer, "Improved Boosting Algorithms Using
Confidence-rated Predictions." Machine Learning, vol. 37, no. 3, pp.
297-336, 1999.
[16] J. Friedman, T. Hastie, and R. Tibshirani, "Additive Logistic Regression:
a Statistical View of Boosting," The Annals of Statistics, vol.28, no.2,
pp.337-407, 2000.
[17] L. L. Huang, A. Shimizu, "A multi-expert approach for robust face
detection," Pattern Recognition, vol. 39, pp. 1695-1703, 2006.
[18] R. L. Hsu, M. A. Mottaleb, A.K. Jain, Face detection in color images,
IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 5, pp. 695- 705,
2002.
[19] A. Cheddad, J. Condell, K. Curran, P. Kevitt, "A skin tone detection
algorithm for an adaptive approach to steganography," Singal Processing,
vol.89, pp. 2465-2478, 2009.
[20] R.C. Gonzalez and R.E. Woods, Digital image processing,
Addison-Wesley Publishing Company, Inc., 1992.
[21] D.P. Huttenlocher, G.A. Klanderman, W.J. Rucklidge, Comparing images
using the Hausdorff distance, IEEE Transactions on Pattern Analysis and
Machine Intelligence, vol. 15, no. 9, pp. 850-863, 1993.
[22] http://www.y28predictions.com/program/index.php
@article{"International Journal of Information, Control and Computer Sciences:54869", author = "Y. H. Tsai and Y. W. Chen", title = "Fast Facial Feature Extraction and Matching with Artificial Face Models", abstract = "Facial features are frequently used to represent local
properties of a human face image in computer vision applications. In
this paper, we present a fast algorithm that can extract the facial
features online such that they can give a satisfying representation of a
face image. It includes one step for a coarse detection of each facial
feature by AdaBoost and another one to increase the accuracy of the
found points by Active Shape Models (ASM) in the regions of interest.
The resulted facial features are evaluated by matching with artificial
face models in the applications of physiognomy. The distance measure
between the features and those in the fate models from the database is
carried out by means of the Hausdorff distance. In the experiment, the
proposed method shows the efficient performance in facial feature
extractions and online system of physiognomy.", keywords = "Facial feature extraction, AdaBoost, Active shapemodel, Hausdorff distance", volume = "5", number = "7", pages = "748-5", }
