Abstract
Localization of facial feature points is an important step for many subsequent facial image analysis tasks. In this paper, we proposed a new coarse-to-fine method for extracting 20 facial feature points from image sequences. In particular, the Viola-Jones face detection method is extended to detect small-scale facial components with wide shape variations, and linear Kalman filters are used to smoothly track the feature points by handling detection errors and head rotations. The proposed method achieved higher than 90% detection rate when tested on the BioID face database and the FG-NET facial expression database. Moreover, our method shows robust performance against the variation of face resolutions and facial expressions.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Pantic, M., Rothkrantz, L.: Expert system for automatic analysis of facial expression. Image and Vision Computing Journal 18, 881–905 (2000)
Wiskott, L., Fellous, J.M., Krüger, N., von der Malsburg, C.: Face recognition by elastic bunch graph matching. In: Jain, L.C., Halici, U., Hayashi, I., Lee, S.B. (eds.) Intelligent Biometric Techniques in Fingerprint and Face Recognition, pp. 355–396. CRC Press, Boca Raton (1999)
Dailey, M.N., Cottrell, G.W.: PCA = gabor for expression recognition. Technical Report CS1999-0629 (1999)
Turk, M., Pentland, A.: Eigenfaces for recognition. Journal of Cognitive Neuroscience 3, 71–76 (1991)
Shih, F.Y., Chuang, C.F.: Automatic extraction of head and face boundaries and facial features. Information Sciences 158, 117–130 (2004)
Ryu, Y.S., Oh, S.Y.: Automatic extraction of eye and mouth fields from a face image using eigenfeatures and ensemble networks. Applied Intelligence 17, 171–185 (2002)
Arca, S., Campadelli, P., Lanzarotti, R.: A face recognition system based on automatically determined facial fiducial points. Pattern Recognition 39, 432–443 (2006)
Campadelli, P., Lanzarotti, R.: Localization of facial features and fiducial points. In: Proceedings of the International Conference on Visualisation, Imaging and image Processing, pp. 491–495 (2002)
Liao, C.T., Wu, Y.K., Lai, S.H.: Locating facial feature points using support vector machines. In: Proceedings of the 9th International Workshop on Cellular Neural Networks and Their Applications, pp. 296–299 (2005)
Zobel, M., Gebhard, A., Paulus, D., Denzler, J., Niemann, H.: Robust facial feature localization by coupled features. In: Proceedings of the 4th International Conference on Automatic Face and Gesture Recognition, pp. 2–7 (2000)
Yan, S., Hou, X., Li, S.Z., Zhang, H., Cheng, Q.: Face alignment using view-based direct appearance models. International Journal of Imaging Systems and Technology 13, 106–112 (2003)
Viola, P., Jones, M.: Robust real-time object detection. International Journal of Computer Vision (2002)
Freund, Y., Schapire, R.E.: A decision-theoretic generalization of online learning and an application to boosting. In: Vitányi, P.M.B. (ed.) EuroCOLT 1995. LNCS, vol. 904, pp. 23–37. Springer, Heidelberg (1995)
Lienhart, R., Maydt, J.: An extended set of haar-like features for rapid object detection. In: Proceedings of the International Conference on Image Processing, vol. 1, pp. I–900–I–903 (2002)
Harris, C., Stephens, M.: A combined corner and edge detector. In: Alvey Vision Conference, pp. 147–151 (1998)
http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhan, C., Li, W., Ogunbona, P., Safaei, F. (2007). Real-Time Facial Feature Point Extraction. In: Ip, H.HS., Au, O.C., Leung, H., Sun, MT., Ma, WY., Hu, SM. (eds) Advances in Multimedia Information Processing – PCM 2007. PCM 2007. Lecture Notes in Computer Science, vol 4810. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77255-2_11
Download citation
DOI: https://doi.org/10.1007/978-3-540-77255-2_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-77254-5
Online ISBN: 978-3-540-77255-2
eBook Packages: Computer ScienceComputer Science (R0)