Face detection using beta wavelet filter and cascade classifier entrained with Adaboost

Face detection has been one of the most studied topics in the computer vision literature due to its relevant role in applications such as video surveillance, human computer interface and face image database management. Here, we will present a face detection approach which contains two steps. The first step is training phase based on Adaboost algorithm. The second step is the detection phase. The proposed approach presents an enhancement of Viola and Jones’ algorithm by replacing Haar descriptors with Beta wavelet. The obtained results have proved an excellent performance of detection not only when a face is in front of the camera but also when it is oriented towards the right or the left. Moreover, thanks to the start period needed for the detection, our approach can be applied during a real time experience.

[1]  Mathias Kölsch,et al.  Analysis of rotational robustness of hand detection with a Viola-Jones detector , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[2]  Jing-Yu Yang,et al.  Face detection using template matching and skin-color information , 2007, Neurocomputing.

[3]  Christopher H. Messom,et al.  Stream processing for fast and efficient rotated Haar-like features using rotated integral images , 2009, Int. J. Intell. Syst. Technol. Appl..

[4]  D. Maio,et al.  Fast Face Location in Complex Backgrounds , 1998 .

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

[6]  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.

[7]  E. M. Speiser,et al.  Advanced Algorithms and Architectures for Signal Processing , 1986 .

[8]  C. Ben Amar,et al.  Training of the Beta wavelet networks by the frames theory: Application to face recognition , 2008, 2008 First Workshops on Image Processing Theory, Tools and Applications.

[9]  Guodong Guo,et al.  Boosting for fast face recognition , 2001, Proceedings IEEE ICCV Workshop on Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems.

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

[11]  Ioannis Pitas,et al.  Robust Watermarking of Facial Images Based on Salient Geometric Pattern Matching , 2000, IEEE Trans. Multim..

[12]  Liang Lu.  Single Rotated Face Location Based on Affine Template Matching , 2000 .

[13]  Chokri Ben Amar,et al.  Beta wavelets. Synthesis and application to lossy image compression , 2005, Adv. Eng. Softw..

[14]  Paul A. Viola,et al.  Detecting Pedestrians Using Patterns of Motion and Appearance , 2005, International Journal of Computer Vision.

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

[16]  Erik Hjelmås,et al.  Face Detection: A Survey , 2001, Comput. Vis. Image Underst..

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