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Joint Integral Histogram based Adaboost for Face Detection System
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 23 - Number 5 |
| Year of Publication: 2011 |
| Authors: Ameni Yengui Jammoussi, Dorra Sellami Masmoudi |
10.5120/2984-3767
|
Ameni Yengui Jammoussi, Dorra Sellami Masmoudi . Joint Integral Histogram based Adaboost for Face Detection System. International Journal of Computer Applications. 23, 5 ( June 2011), 38-43. DOI=10.5120/2984-3767
Abstract
Face detection is a crucial step in many vision applica-tions. Since the Viola and Jones face detector, many fea- ture extraction approches based Adaboost are proposed.This paper presents a novel approach to extract effective features for face detection system. Both LBP and three Patch LBP (TPLBP) with joint integral histogram are used to extract features. The joint integral histogram was firstly proposed for stereo matching application. Its effectiveness has motivated us to apply it harnessing its advantages. The evaluation of the novel features based Adaboost was done using the CMU-MIT frontal face data set. Experimental results show that its performance is noteworthy specially for the earlier stages. In fact, with few number of the new features we can achieve the max detection (1) and reduced false positive rate (0.28).
References
- P. Viola and M. Jones, “Robust Real-time Object Detection,” Second InternationalWorkshop on Statistical and Computational theories of vision modeling, learning, computing and sampling, Vancouver, Canada, July. 2001.
- T. Ojala, M. Pietikainen and T. Maenpaa, ”Multiresolution Gray-Scale and Rotation Invariant Tex- ture Classification with Local Binary Patterns,” IEEE Transaction On Pattern Analysis And Machine Intelli- gence, VOL. 24, NO. 7, July 2002.
- L. Zhang, R.Chu, S.Xiang, S.Liao and S. Z. Li, ”Face Detection Based on Multi-Block LBP Representation,” , pp.11–18, 2007.
- S. Yan, S. Shan, X. Chen and W. Gao, ”Locally Assembled Binary (LAB) Feature with Feature-centric Cascade for Fast and Accurate Face Dtetcion,” , China, 2008.
- A. Roy and S. Marcel, ”Haar Local Binary Pattern Feature for Fast Illumination Invariant Face Detection,” , Lausane, Switzerland, 2009.
- L.Wolf, T.Hassner and Y.Taigman, ”Descriptor Based Methods in the Wild,” , Israel, 2008.
- P. Viola and M.J. Jones, ”Robust Real-time Face Detection,” International Journal of Computer Vision, vol. 57, no. 2, pp. 137–154, Netherlands, 2004.
- P. Viola and M.J. Jones, ”Robust Real-time Object Detection,” Second InternationalWorkshop on Statistical and Computational theories of vision modeling, learning, computing and sampling, vol. 1, no. 3, pp. 1–10, July, 2001.
- H. Wang, P. Li and T. Zhang, ”Histogram feature based Fisher linear discriminant for face detection,”in Neural Comput and Applic, January, 2007.
- K. Zhang, G. Lafruit, R. Lauwereins and L.V. Gool, ”Joint Integral Histogram and its Application in Stereo Matching,” Proceeding of 2010 IEEE 17th International Conference on Image Processing, Hong Kong, September 26-29, 2010.
- J. Trefny and J.Matas, ”Extended Set of Local Binary Patterns For Rapid Object Dtection,” Computer Vision Winter Workshop 2010, Libor ˜ Spa˜cek, Vojt˜ech Franc (eds.), Czech Republic, February 2010.
- Anusha Bamini.A.M and Kavitha.T, ”Dominant Local Binary Pattern Based Face Feature Selection and Detection,” International Journal of Engineering and technology, 2010, vol. 2(2), pp. 77–80.
- S. Paisitkriangkrai, C. Shen and J.Zhang, ”Face Detetcion with Effective Feature Extraction,” CoRR, 2010.
- Jo Chang-yeon, ”Face Dtection using LBP features,” Final Project Report, December 2008.
- C. Zhang and Z. Zhang, ”A Survey of Recent Advances in Face Detection,” Technical Report, June 2010.
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