A Contrario Detection of Faces with a Short Cascade of Classifiers

The a contrario framework has been successfully used for the detection of lines, contours and other meaningful structures in digital images. In this paper we describe the implementation of an algorithm for face detection published in 2017 by Lisani et al. which applies the a contrario approach to the computation of the detection thresholds of a classical cascade of classifiers. The result is a very short cascade which obtains similar detection rates than a classical (and longer) one at a much lower computational cost.

[1]  Stefanos Zafeiriou,et al.  A survey on face detection in the wild: Past, present and future , 2015, Comput. Vis. Image Underst..

[2]  Jose Luis Lisani,et al.  Fast video search and indexing for video surveillance applications with optimally controlled False Alarm Rates , 2011, 2011 IEEE International Conference on Multimedia and Expo.

[3]  Lionel Moisan,et al.  Edge Detection by Helmholtz Principle , 2001, Journal of Mathematical Imaging and Vision.

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

[5]  Shuo Yang,et al.  WIDER FACE: A Face Detection Benchmark , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[6]  Malcolm O. Asadoorian,et al.  Essentials of Inferential Statistics , 2004 .

[7]  Rafael Grompone von Gioi,et al.  Unsupervised Smooth Contour Detection , 2016, Image Process. Line.

[8]  Julie Delon,et al.  A Nonparametric Approach for Histogram Segmentation , 2007, IEEE Transactions on Image Processing.

[9]  Francisco J. Perales López,et al.  A Contrario Detection of Faces: A Case Example , 2017, SIAM J. Imaging Sci..

[10]  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..

[11]  Yi-Qing Wang,et al.  An Analysis of the Viola-Jones Face Detection Algorithm , 2014, Image Process. Line.

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

[13]  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.

[14]  Jose Luis Lisani,et al.  Detection of major changes in satellite images , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).

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

[16]  Rainer Lienhart,et al.  Empirical Analysis of Detection Cascades of Boosted Classifiers for Rapid Object Detection , 2003, DAGM-Symposium.

[17]  Erik Learned-Miller,et al.  FDDB: A benchmark for face detection in unconstrained settings , 2010 .

[18]  Erik G. Learned-Miller,et al.  Online domain adaptation of a pre-trained cascade of classifiers , 2011, CVPR 2011.

[19]  Horst Bischof,et al.  Robust face detection by simple means , 2012 .

[20]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.