Real-Time Facial Feature Point Extraction

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.

[1]  Alan L. Yuille,et al.  Feature extraction from faces using deformable templates , 2004, International Journal of Computer Vision.

[2]  Frank Y. Shih,et al.  Automatic extraction of head and face boundaries and facial features , 2004, Inf. Sci..

[3]  Maja Pantic,et al.  Expert system for automatic analysis of facial expressions , 2000, Image Vis. Comput..

[4]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

[5]  U. Halici,et al.  Intelligent biometric techniques in fingerprint and face recognition , 2000 .

[6]  M. Turk,et al.  Eigenfaces for Recognition , 1991, Journal of Cognitive Neuroscience.

[7]  Paola Campadelli LOCALIZATION OF FACIAL FEATURES AND FIDUCIAL POINTS , 2002 .

[8]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, International Journal of Computer Vision.

[9]  Se-Young Oh,et al.  Automatic Extraction of Eye and Mouth Fields from a Face Image Using Eigenfeatures and Ensemble Networks , 2004, Applied Intelligence.

[10]  Paul A. Viola,et al.  Robust Real-time Object Detection , 2001 .

[11]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.

[12]  Shang-Hong Lai,et al.  Locating facial feature points using support vector machines , 2005, 2005 9th International Workshop on Cellular Neural Networks and Their Applications.

[13]  Norbert Krüger,et al.  Face recognition by elastic bunch graph matching , 1997, Proceedings of International Conference on Image Processing.

[14]  Shuicheng Yan,et al.  Face alignment using view‐based direct appearance models , 2003, Int. J. Imaging Syst. Technol..

[15]  Joachim Denzler,et al.  Robust facial feature localization by coupled features , 2000, Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580).

[16]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1997, EuroCOLT.

[17]  Garrison W. Cottrell,et al.  PCA = Gabor for Expression Recognition , 1999 .

[18]  Rainer Lienhart,et al.  An extended set of Haar-like features for rapid object detection , 2002, Proceedings. International Conference on Image Processing.

[19]  Paola Campadelli,et al.  A face recognition system based on automatically determined facial fiducial points , 2006, Pattern Recognit..