Face detection using look-up table based gentle adaboost

Authors:
Cem Demirkır;Bülent Sankur
Affiliations:
Electrical-Electronic Engineering Department, Boğaziçi University, Bebek, İstanbul;Electrical-Electronic Engineering Department, Boğaziçi University, Bebek, İstanbul
Venue:
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
Year:
2005

Citing 2
Cited 4

Statistical Learning of Multi-view Face Detection

ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Fast rotation invariant multi-view face detection based on real adaboost

FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition

A robust multi-class traffic sign detection and classification system using asymmetric and symmetric features

SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Analysis of head and facial gestures using facial landmark trajectories

BioID_MultiComm'09 Proceedings of the 2009 joint COST 2101 and 2102 international conference on Biometric ID management and multimodal communication
Robust classification of face and head gestures in video

Image and Vision Computing
Real-time object detection on CUDA

Journal of Real-Time Image Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this work, we propose a face detection method based on the Gentle AdaBoost algorithm which is used for construction of binary tree structured strong classifiers. Gentle AdaBoost algorithm update values are constructed by using the difference of the conditional class probabilities for the given value of Haar features proposed by [1]. By using this approach, a classifier which can model image classes that have high degree of in-class variations can be constructed and the number of required Haar features can be reduced.