A Novel Facial Feature Localization Method Using Probabilistic-like Output *

Object detection technique generally could not localize facial features precisely because there is a tradeoff between detection robustness and facial feature support [2]. To address this problem, we propose a method to calculate probabilistic-like output for each pixel of image. This output describes the similarity of a patch of image to the training samples. The probabilistic-like output is afterwards used to locate the feature points using a localization approach we proposed. Our algorithm for facial feature localization is fast, accurate and robust. It takes only about 10ms on a computer with P4 CPU to locate five feature points including eye centers, nose tip and mouth corners. The localization accuracy is comparable with hand labeled results. And experimental results on a large size of face database (more than 12,000 images from PIE [10]) demonstrate that the proposed method is robust to the variances of pose, illumination, expression and other appearance factors, such as glasses and beard.

[1]  Shaoyan Zhang,et al.  Face recognition with support vector machine , 2003, IEEE International Conference on Robotics, Intelligent Systems and Signal Processing, 2003. Proceedings. 2003.

[2]  Takeo Kanade,et al.  Automated facial expression recognition based on FACS action units , 1998, Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition.

[3]  Rogério Schmidt Feris,et al.  Hierarchical wavelet networks for facial feature localization , 2002, Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition.

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

[5]  Harry Wechsler,et al.  The FERET database and evaluation procedure for face-recognition algorithms , 1998, Image Vis. Comput..

[6]  Terence Sim,et al.  The CMU Pose, Illumination, and Expression (PIE) database , 2002, Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition.

[7]  Rong Xiao,et al.  Boosting chain learning for object detection , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[8]  Wen Gao,et al.  Localizing the iris center by region growing search , 2002, Proceedings. IEEE International Conference on Multimedia and Expo.

[9]  N. Magnenat-Thalmann,et al.  Automatic face cloning and animation using real-time facial feature tracking and speech acquisition , 2001, IEEE Signal Process. Mag..

[10]  Yoav Freund,et al.  A Short Introduction to Boosting , 1999 .

[11]  Margrit Betke,et al.  Communication via eye blinks - detection and duration analysis in real time , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.