An Improved Adaboost Method for Face Detection

Zhi Xun Zhang; Juan Wang; Yong Dong Wang

doi:10.4028/www.scientific.net/AMM.599-601.807

Paper Titles

A Survey on Moving Target Tracking in the Intelligent Visual Monitoring System
p.790

Adaptive Fuzzy PID Control Applied in Boiler System of Power Plant
p.794

An Approach for Detecting Illegal Load in Wireless Power Transfer System
p.798

An Image Positioning Method of Automatic Random Walker Based on IFS Edge Detection
p.802

An Improved Adaboost Method for Face Detection
p.807

An Improved Algorithm for Real-Time Moving Target Detection Based on Gaussian Mixture Model
p.814

Analysis and Research of Rogowski Coil Current Sensing Technology
p.819

Analysis of Air Power Switch Work under Harmonic
p.823

Application of BP Neural Network PID Algorithm in Refrigerated Space Temperature Control
p.827

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 599-601An Improved Adaboost Method for Face Detection

An Improved Adaboost Method for Face Detection

Abstract:

Existing Adaboost methods for face detection based on particle swarm optimization (PSO) do not consider that PSO suffers from easily trapping in local optimum and slow convergence speed. This paper presents an improved Adaboost method for face detection to solve this problem. In this method, self-adaptive escape PSO (AEPSO) is introduced into conventional Adaboost face detection, meanwhile, Haar-Like rectangular features are represented by particles, so that features selection and classifiers construction could be resolved by using AEPSO. Results of simulation based on Matlab indicate the improved method obtains better detection performance.

You might also be interested in these eBooks

Frontiers of Manufacturing Science and Measuring Technology IV

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 599-601)

Pages:

807-813

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.599-601.807

Citation:

Cite this paper

Online since:

August 2014

Authors:

Zhi Xun Zhang*, Juan Wang, Yong Dong Wang

Keywords:

Adaboost, AEPSO, Face Detection, Haar-Like Rectangular Features, PSO

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] ZHANG C, ZHANG Z Y. A survey of recent advances in face detection [R]. Microsoft Research Technical Report, MSR-TR-2010-66, (2010).

Google Scholar

[2] VIOLA P, JONES M J. Robust real-time face detection [J]. International Journal of Computer Vision, 2004, 57(2)：137-154.

DOI: 10.1023/b:visi.0000013087.49260.fb

Google Scholar

[3] FREUND Y, SCHAPIRE R E. A decision-theoretic generalization of online learning and an application to boosting [J]. Journal of Computer and System Sciences, 1997, 55(1)：119-139.

DOI: 10.1006/jcss.1997.1504

Google Scholar

[4] KENNEDY J, EBERHART R. Particle Swarm Optimization [C] / IEEE International Conference on Neural Networks. Piscataway：IEEE Service Center, 1995：1942-(1948).

Google Scholar

[5] HIDAKA A, KURITA T. Fast training algorithm by Particle Swarm Optimization and random candidate selection for rectangular feature based Boosted detector [C] / Proceedings of 2008 IEEE International Joint Conference on Neural Networks (2008).

DOI: 10.1109/ijcnn.2008.4633946

Google Scholar

[6] MOHEMMED A W, ZHANG M J, JOHNSTON M. Particle Swarm Optimization based Adaboost for face detection [C] / Proceedings of IEEE Congress on Evolutionary Computation. Picataway, NJ：IEEE Press, 2009：2494-2501.

DOI: 10.1109/cec.2009.4983254

Google Scholar

[7] HE R, WANG Y J, WANG Q, et al. An improved Particle Swarm Optimization based on self-adaptive escape velocity [J]. Journal of Software, 2005, 16(12)：2036-2044(in Chinese with English abstract). http: /www. jos. org. cn/1000-9825/16/2036. htm[doi: 10. 1360/jos16203.

DOI: 10.1360/jos162036

Google Scholar