A New Method for Haar-Like Features Weight Adjustment Using Principal Component Analysis for Face Detection

This paper proposes a new weight assignment method for Haar-like features. The method uses principal component analysis (PCA) over the positive training instances to assign new weights to the features. Together with the method, a particular Haar-like feature that uses statistics extracted from positive training instances is employed. The method and the Haar-like feature were designed to verify if the distribution of points produced from the negative instances in the single rectangle feature space (SRFS) of each Haar-like feature could be modeled as an uniform distribution. Although negative instances may spread themselves in very different and chaotic ways through the SRFS, experiment with the method and the Haar-like feature has shown that the negative instance cannot be properly modeled as an uniform distribution.

[1]  A. Haar.  Zur Theorie der orthogonalen Funktionensysteme , 1910 .

[2]  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.

[3]  Klaus J. Kirchberg,et al.  Robust Face Detection Using the Hausdorff Distance , 2001, AVBPA.

[4]  Tomaso A. Poggio,et al.  A general framework for object detection , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[5]  Aleix M. Martinez,et al.  The AR face database , 1998 .

[6]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[7]  Oscar Déniz-Suárez,et al.  A comparison of face and facial feature detectors based on the Viola–Jones general object detection framework , 2011, Machine Vision and Applications.

[8]  Shree K. Nayar,et al.  Pattern rejection , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[9]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

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

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

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

[13]  José Luis Alba-Castro,et al.  The Role of Polarity in Haar-like Features for Face Detection , 2010, 2010 20th International Conference on Pattern Recognition.

[14]  Narendra Ahuja,et al.  A SNoW-Based Face Detector , 1999, NIPS.

[15]  Zhengyou Zhang,et al.  A Survey of Recent Advances in Face Detection , 2010 .

[16]  Florian Baumann,et al.  Symmetry Enhanced Adaboost , 2010, ISVC.

[17]  Alejandro F. Frangi,et al.  Haar-like features with optimally weighted rectangles for rapid object detection , 2010, Pattern Recognition.

[18]  Tatsuo Arai,et al.  Multi-view fast object detection by using extended haar filters in uncontrolled environments , 2012, Pattern Recognit. Lett..

[19]  Zhenyu Wang,et al.  A novel distribution-based feature for rapid object detection , 2011, Neurocomputing.

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

[21]  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).

[22]  Robert E. Schapire,et al.  The Boosting Approach to Machine Learning An Overview , 2003 .

[23]  James Reinders,et al.  Intel threading building blocks - outfitting C++ for multi-core processor parallelism , 2007 .

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

[25]  Sandeep Koranne,et al.  Boost C++ Libraries , 2011 .

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

[27]  Conrad Sanderson,et al.  Armadillo: An Open Source C++ Linear Algebra Library for Fast Prototyping and Computationally Intensive Experiments , 2010 .

[28]  Jerzy Dembski.  Feature Type and Size Selection for AdaBoost Face Detection Algorithm , 2010, IP&C.

[29]  Alejandro F. Frangi,et al.  Gaussian Weak Classifiers Based on Haar-Like Features with Four Rectangles for Real-time Face Detection , 2009, CAIP.