Fast and Robust Face Detection Using Evolutionary Pruning

Face detection task can be considered as a classifier training problem. Finding the parameters of the classifier model by using training data is a complex process. To solve such a complex problem, evolutionary algorithms can be employed in cascade structure of classifiers. This paper proposes evolutionary pruning to reduce the number of weak classifiers in AdaBoost-based cascade detector, while maintaining the detection accuracy. The computation time is proportional to the number of weak classifiers and, therefore, a reduction in the number of weak classifiers results in an increased detection speed. Three kinds of cascade structures are compared by the number of weak classifiers. The efficiency in computation time of the proposed cascade structure is shown experimentally. It is also compared with the state-of-the-art face detectors, and the results show that the proposed method outperforms the previous studies. A multiview face detector is constructed by incorporating the three face detectors: frontal, left profile, and right profile.

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

[2]  James M. Rehg,et al.  Linear Asymmetric Classifier for cascade detectors , 2005, ICML.

[3]  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..

[4]  Jong-Hwan Kim,et al.  Evolutionary Pruning for Fast and Robust Face Detection , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[5]  Thomas Bäck,et al.  Evolutionary computation: comments on the history and current state , 1997, IEEE Trans. Evol. Comput..

[6]  Jong-Hwan Kim,et al.  Evolutionary algorithm-based face verification , 2004, Pattern Recognit. Lett..

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

[8]  Chengjun Liu,et al.  Evolutionary Pursuit and Its Application to Face Recognition , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Thomas Bäck,et al.  Evolutionary algorithms in theory and practice - evolution strategies, evolutionary programming, genetic algorithms , 1996 .

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

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

[12]  Hans-Paul Schwefel,et al.  Evolution and optimum seeking , 1995, Sixth-generation computer technology series.

[13]  Fabien Moutarde,et al.  COMBINING ADABOOST WITH A HILL-CLIMBING EVOLUTIONARY FEATURE SEARCH FOR EFFICIENT TRAINING OF PERFORMANT VISUAL OBJECT DETECTORS , 2006 .

[14]  Sayan Mukherjee,et al.  Feature reduction and hierarchy of classifiers for fast object detection in video images , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

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

[16]  Franklin C. Crow,et al.  Summed-area tables for texture mapping , 1984, SIGGRAPH.

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

[18]  Wei Tang,et al.  Ensembling neural networks: Many could be better than all , 2002, Artif. Intell..

[19]  James M. Rehg,et al.  Learning a Rare Event Detection Cascade by Direct Feature Selection , 2003, NIPS.

[20]  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).

[21]  Andreas Zell,et al.  Combining Adaboost learning and evolutionary search to select features for real-time object detection , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[22]  Xiao Wang,et al.  Classification by evolutionary ensembles , 2006, Pattern Recognit..

[23]  Mingjing Li,et al.  Robust multipose face detection in images , 2004, IEEE Transactions on Circuits and Systems for Video Technology.

[24]  Jiri Matas,et al.  Inter-stage feature propagation in cascade building with AdaBoost , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..