A robust adaboost-based algorithm for low-resolution face detection

Authors:
Diego Alonso Fernández Merjildo;Lee Luan Ling
Affiliations:
Department of Communications, DECOM, School of Electrical and Computer Engineering, FEEC, State University of Campinas, UNICAMP, Campinas, Brazil;Department of Communications, DECOM, School of Electrical and Computer Engineering, FEEC, State University of Campinas, UNICAMP, Campinas, Brazil
Venue:
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
Year:
2012

Citing 7
Cited 0

Soft Margins for AdaBoost

Machine Learning
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns

IEEE Transactions on Pattern Analysis and Machine Intelligence
Ensemble Methods in Machine Learning

MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Smooth boosting and learning with malicious noise

The Journal of Machine Learning Research
Robust Real-Time Face Detection

International Journal of Computer Vision
Detecting faces from low-resolution images

ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part I
Learning multi-scale block local binary patterns for face recognition

ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics

Quantified Score

Hi-index	0.00

Visualization

Abstract

This work presents a face detection algorithm based on Multiscale Block Local Binary Patterns (MB-LBP) and an improved AdaBoost algorithm. The proposed boosting algorithm is capable of avoiding sample overfitting over its training process. This goal is achieved by making use of the information of sample misclassification frequency to update the weight distribution in the training process. Experimental results evidence some advantages of the proposed method over the classical AdaBoost algorithms, including the generalization capacity, overfitting avoidance and high precision rate on low-resolution images.