High-Performance Rotation Invariant Multiview Face Detection

Rotation invariant multiview face detection (MVFD) aims to detect faces with arbitrary rotation-in-plane (RIP) and rotation-off-plane (ROP) angles in still images or video sequences. MVFD is crucial as the first step in automatic face processing for general applications since face images are seldom upright and frontal unless they are taken cooperatively. In this paper, we propose a series of innovative methods to construct a high-performance rotation invariant multiview face detector, including the width-first-search (WFS) tree detector structure, the vector boosting algorithm for learning vector-output strong classifiers, the domain-partition-based weak learning method, the sparse feature in granular space, and the heuristic search for sparse feature selection. As a result of that, our multiview face detector achieves low computational complexity, broad detection scope, and high detection accuracy on both standard testing sets and real-life images

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

[2]  Yoav Freund,et al.  Boosting a weak learning algorithm by majority , 1995, COLT '90.

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

[4]  Nils J. Nilsson,et al.  Artificial Intelligence: A New Synthesis , 1997 .

[5]  Yoav Freund,et al.  Boosting the margin: A new explanation for the effectiveness of voting methods , 1997, ICML.

[6]  Federico Girosi,et al.  Training support vector machines: an application to face detection , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

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

[8]  Takeo Kanade,et al.  Rotation invariant neural network-based face detection , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[9]  Yoram Singer,et al.  Improved Boosting Algorithms Using Confidence-rated Predictions , 1998, COLT' 98.

[10]  Yoav Freund,et al.  An Adaptive Version of the Boost by Majority Algorithm , 1999, COLT '99.

[11]  Takeo Kanade,et al.  A statistical method for 3D object detection applied to faces and cars , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[12]  J. Friedman.  Special Invited Paper-Additive logistic regression: A statistical view of boosting , 2000 .

[13]  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.

[14]  Harry Shum,et al.  Statistical Learning of Multi-view Face Detection , 2002, ECCV.

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

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

[17]  Paul A. Viola,et al.  Fast Multi-view Face Detection , 2003 .

[18]  Harry Shum,et al.  Kullback-Leibler boosting , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

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

[20]  Yann LeCun,et al.  Synergistic Face Detection and Pose Estimation with Energy-Based Models , 2004, J. Mach. Learn. Res..

[21]  Bo Wu,et al.  Fast rotation invariant multi-view face detection based on real Adaboost , 2004, Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings..

[22]  Donald Geman,et al.  Coarse-to-Fine Face Detection , 2004, International Journal of Computer Vision.

[23]  Bruno Steux,et al.  YEF∗Real-Time Object Detection , 2004 .

[24]  David P. Helmbold,et al.  Boosting Methods for Regression , 2002, Machine Learning.

[25]  Shumeet Baluja,et al.  Efficient face orientation discrimination , 2004, 2004 International Conference on Image Processing, 2004. ICIP '04..

[26]  Shaogang Gong,et al.  Support vector machine based multi-view face detection and recognition , 2004, Image Vis. Comput..

[27]  Qiang Ji,et al.  Learning discriminant features for multi-view face and eye detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[28]  Takeshi Mita,et al.  Joint Haar-like features for face detection , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[29]  Yuan Li,et al.  Vector boosting for rotation invariant multi-view face detection , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[30]  Yuan Li,et al.  Learning sparse features in granular space for multi-view face detection , 2006, 7th International Conference on Automatic Face and Gesture Recognition (FGR06).

[31]  Bruno Steux,et al.  Yet Even Faster (YEF) real-time object detection , 2007, Int. J. Intell. Syst. Technol. Appl..