Robust Face Detection Using Multi-Block Local Gradient Patterns and Extreme Learning Machine

A novel multi-block local gradient patterns (MB-LGP) based face detection method was proposed in this article. The MB-LGP operators extract face features in the way similar to local gradient patterns (LGP) however, the gradient of pixels in LGP was replaced by the counterparts of square image areas in MB-LGP. We have proved that the MB-LGP has most of the advantages of LGP and moreover with a stronger discriminant power and better robustness against noise. In the classification part, the extreme learning machine was introduced in the last stage in the proposed cascade classifier in order to speed up training process and increase classification accuracy. As was shown in experiments using the CMU\(+\)MIT database the new method possesses high detection rate.

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

[2]  Ramin Zabih,et al.  Non-parametric Local Transforms for Computing Visual Correspondence , 1994, ECCV.

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

[4]  Xiaoyang Tan,et al.  Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions , 2007, IEEE Transactions on Image Processing.

[5]  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.

[6]  Stan Z. Li,et al.  FloatBoost learning and statistical face detection , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Hongming Zhou,et al.  Extreme Learning Machine for Regression and Multiclass Classification , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[9]  Yong Dou,et al.  A Reconfigurable Architecture for Rotation Invariant Multi-View Face Detection Based on a Novel Two-Stage Boosting Method , 2009, EURASIP J. Adv. Signal Process..

[10]  Matti Pietikäinen,et al.  Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Guang-Bin Huang,et al.  Extreme learning machine: a new learning scheme of feedforward neural networks , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

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

[13]  Yuhua Tang,et al.  Parallization of Adaboost Algorithm through Hybrid MPI/OpenMP and Transactional Memory , 2011, 2011 19th International Euromicro Conference on Parallel, Distributed and Network-Based Processing.

[14]  Shu Liao,et al.  Face Recognition by Using Elongated Local Binary Patterns with Average Maximum Distance Gradient Magnitude , 2007, ACCV.

[15]  Jianming Zhao,et al.  A Vehicle Classification Approach Based on Edge Orientation Histograms , 2012 .

[16]  Jean-Yves Ramel,et al.  Comparison between 2D and 3D Local Binary Pattern Methods for Characterisation of Three-Dimensional Textures , 2008, ICIAR.

[17]  Hanqing Lu,et al.  Face detection using improved LBP under Bayesian framework , 2004, Third International Conference on Image and Graphics (ICIG'04).

[18]  Daijin Kim,et al.  Robust face detection using local gradient patterns and evidence accumulation , 2012, Pattern Recognit..

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