Human Face Processing with 1.5D Models

Integral projections reduce the size of input data by transforming 2D images into significantly simpler 1D signals, while retaining useful information to solve important computer vision problems like object detection, location, and tracking. However, previous attempts typically rely on simple heuristic analysis such as searching for minima or maxima in the resulting projections. We introduce a more rigorous and formal modeling framework based on a small set of integral projections -thus, we will call them 1.5D models- and show that this model-based analysis overcomes many of the difficulties and limitations of alternative projection methods. The proposed approach proves to be particularly adequate for the specific domain of human face processing. The problems of face detection, facial feature location, and tracking in video sequences are studied under the unifying proposed framework.

[1]  Ioannis Pitas,et al.  Rule-based face detection in frontal views , 1997, 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[2]  S. Deans.  The Radon Transform and Some of Its Applications , 1983 .

[3]  Ginés García Mateos.  Refining face tracking with integral projections , 2003 .

[4]  Takeo Kanade,et al.  Neural Network-Based Face Detection , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Pong C. Yuen,et al.  Variance projection function and its application to eye detection for human face recognition , 1998, Pattern Recognit. Lett..

[6]  Gary R. Bradski,et al.  Real time face and object tracking as a component of a perceptual user interface , 1998, Proceedings Fourth IEEE Workshop on Applications of Computer Vision. WACV'98 (Cat. No.98EX201).

[7]  Peyman Milanfar,et al.  Fast Local and Global Projection-Based Methods for Affine Motion Estimation , 2004, Journal of Mathematical Imaging and Vision.

[8]  J.-Y. Bouguet,et al.  Pyramidal implementation of the lucas kanade feature tracker , 1999 .

[9]  Richard O. Duda,et al.  Use of the Hough transformation to detect lines and curves in pictures , 1972, CACM.

[10]  John A. Nelder,et al.  A Simplex Method for Function Minimization , 1965, Comput. J..

[11]  Jack D. Gaskill,et al.  Linear systems, fourier transforms, and optics , 1978, Wiley series in pure and applied optics.

[12]  Ning Wang,et al.  Robust precise eye location under probabilistic framework , 2004, Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings..

[13]  Dmitry O. Gorodnichy,et al.  Video-based framework for face recognition in video , 2005, The 2nd Canadian Conference on Computer and Robot Vision (CRV'05).

[14]  Ioannis Pitas,et al.  Looking for Faces and Facial Features in Color Images , 1997 .

[15]  Tomaso A. Poggio,et al.  Example-Based Learning for View-Based Human Face Detection , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  Yajie Tian,et al.  Handbook of face recognition , 2003 .

[17]  Narendra Ahuja,et al.  Detecting Faces in Images: A Survey , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  Takeo Kanade,et al.  Picture Processing System by Computer Complex and Recognition of Human Faces , 1974 .

[19]  Alex Pentland,et al.  View-based and modular eigenspaces for face recognition , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[20]  Hyeonjoon Moon,et al.  The FERET Evaluation Methodology for Face-Recognition Algorithms , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[21]  Bjarne K. Ersbøll,et al.  FAME-a flexible appearance modeling environment , 2003, IEEE Transactions on Medical Imaging.

[22]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.