A robustness and real-time face detection algorithm in complex background

Because AdaBoost Cascade face detection algorithm has a very outstanding performance, AdaBoost face detection is the mainstream algorithm currently. But it can produce misjudgment at a similar facial feature regional, particularly in the detection of more complicated image background circumstances misjudgment is even more serious. In view of reasons above, in this paper, a new algorithm was proposed and named A-SCS algorithm, which is increased skin color segmentation after detected face region use the AdaBoost algorithm. This algorithm makes full use of the image useful information, and greatly reduced the possibility of misjudgment. Compare to AdaBoost algorithm and skin color segmentation algorithm, the algorithm mentioned in this paper reduced the false detecting rate in complex background image, At the same time, it is of definite robustness. Simulated experimental results by Matlab indicate that this algorithm is faster and accuracy. Therefore it can be applied to real-time face detection system.

[1]  Luhong Liang,et al.  A detector tree of boosted classifiers for real-time object detection and tracking , 2003, 2003 International Conference on Multimedia and Expo. ICME '03. Proceedings (Cat. No.03TH8698).

[2]  Rama Chellappa,et al.  Human and machine recognition of faces: a survey , 1995, Proc. IEEE.

[3]  James M. Rehg,et al.  Statistical Color Models with Application to Skin Detection , 2004, International Journal of Computer Vision.

[4]  Gábor Lugosi,et al.  A Consistent Strategy for Boosting Algorithms , 2002, COLT.

[5]  Qiang Ji,et al.  An automated face reader for fatigue detection , 2004, Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings..

[6]  Yoav Freund,et al.  Experiments with a New Boosting Algorithm , 1996, ICML.

[7]  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.