A fast face detection method based on improved sample selection

Authors:
Weisheng Li;Li Li
Affiliations:
College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China;College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China
Venue:
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 5
Year:
2009

Citing 6
Cited 0

A theory of the learnable

Communications of the ACM
Cryptographic limitations on learning Boolean formulae and finite automata

Journal of the ACM (JACM)
A decision-theoretic generalization of on-line learning and an application to boosting

Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Improved Boosting Algorithms Using Confidence-rated Predictions

Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Robust Real-Time Face Detection

International Journal of Computer Vision
A Real generalization of discrete AdaBoost

Artificial Intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

AdaBoost algorithm is an efficient face detection method whose effectiveness is mainly influenced by the selection of weak classifier during the early process of training. To some extent, the selection of weak classifier depends on the selected sample set. Thus the training sample set is one of the most important factors in face detection. In this paper, the relationship between cascade classifier and weak classifier is analyzed in detail. Based on the factors of detection rate, undetected rate and false detection rate, an improved sample selection method is present and a fast face detection method which is divided into training and detection is proposed. The method is capable of optimizing the proportion of training samples and merging the detection window. The experimental results show that the proposed method is more effective than traditional ones.