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Reseach Article

Efficient Face Detection using Adaboost

Published on March 2012 by K.T.Talele, Sunil Kadam, Atul Tikare
International Conference in Computational Intelligence
Foundation of Computer Science USA
ICCIA - Number 10
March 2012
Authors: K.T.Talele, Sunil Kadam, Atul Tikare
8d138dd5-02ad-49fc-b29b-09f1f1a34108

K.T.Talele, Sunil Kadam, Atul Tikare . Efficient Face Detection using Adaboost. International Conference in Computational Intelligence. ICCIA, 10 (March 2012), 16-20.

@article{
author = { K.T.Talele, Sunil Kadam, Atul Tikare },
title = { Efficient Face Detection using Adaboost },
journal = { International Conference in Computational Intelligence },
issue_date = { March 2012 },
volume = { ICCIA },
number = { 10 },
month = { March },
year = { 2012 },
issn = 0975-8887,
pages = { 16-20 },
numpages = 5,
url = { /proceedings/iccia/number10/5164-1076/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference in Computational Intelligence
%A K.T.Talele
%A Sunil Kadam
%A Atul Tikare
%T Efficient Face Detection using Adaboost
%J International Conference in Computational Intelligence
%@ 0975-8887
%V ICCIA
%N 10
%P 16-20
%D 2012
%I International Journal of Computer Applications
Abstract

Face detection is an essential application of visual object detection and it is one of the main components of face analysis and understanding with face localization and face recognition. It becomes a more and more complex domain used in a large number of applications, among which we find security, new communication interfaces, biometrics and many others. The goal of face detection is to detect human faces in still images or videos, in different situations. We will focus on a detector which processes images very quickly while achieving high detection rates. This detection is based on a boosting algorithm called AdaBoost and the response of simple Haar-based features used by Viola and Jones.

References
  1. P. Viola and M. Jones, “Rapid Object Detection Using a Boosted Cascade of Simple Features,” Computer Vision and Pattern Recognition, 2001.
  2. K.T. Talele, Sunil Kadam, “Face Detection and Geometric Face Normalization,” TENCON,2009, SINGAPORE.
  3. C. Papageorgiou, M. Oren, and T. Poggio, “A general framework for object detection,” In International Conference on Computer vision, 1998.
  4. F. Crow, “Summed-area tables for texture mapping,” In Proceedings of SIGGRAPH, vol. 18(3), pp. 207-212, 1984. (a) Random Image 1 (b) Random Image 2 Fig. 5. Output of face detection on random images
  5. Qiong Wang, Jingyu Yang, “Eye Detection in Facial Images with Unconstrained Background,” Journal of Pattern Recognition Research 1, pp. 55-62, 2006.
  6. Ming-Hsuan Yang, David J. Kriegman, and Narendra Ahuja, “Detecting Faces in Images: A Survey” IEEE Transactions on pattern analysis and machine intelligence, vol. 24, no. 1, January 2002.
  7. S. Fahlman and C. Lebiere, ‘The Cascade-Correlation Learning Architecture,’ Advances in Neural Information Processing Systems 2, D.S. Touretsky, ed., pp. 524-532, 1990.
  8. P. Viola and M. Jones, ‘Fast and Robust Classification using Asymmetric AdaBoost and a Detector Cascade,’ Mitsubishi Electric Research Lab, Cambridge, MA. 2001.
Index Terms

Computer Science
Information Sciences

Keywords

Face detection Adaboost Classifiers Cascade