Gender classification based on boosting local binary pattern

Authors:
Ning Sun;Wenming Zheng;Changyin Sun;Cairong Zou;Li Zhao
Affiliations:
Research Center of Learning Science, Southeast University, Nanjing, China;Department of Radio Engineering, Southeast University, Nanjing, China;College of Electrical Engineering, Hohai University, Nanjing, Jiangsu, China;Department of Radio Engineering, Southeast University, Nanjing, China;Research Center of Learning Science, Southeast University, Nanjing, China
Venue:
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
Year:
2006

Citing 6
Cited 7

SexNet: A neural network identifies sex from human faces

NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
Boosting a weak learning algorithm by majority

Information and Computation
BoosTexter: A Boosting-based Systemfor Text Categorization

Machine Learning - Special issue on information retrieval
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns

IEEE Transactions on Pattern Analysis and Machine Intelligence
Gender Classification with Support Vector Machines

FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Face recognition using ada-boosted gabor features

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

An experimental comparison of gender classification methods

Pattern Recognition Letters
Personal identification using periocular skin texture

Proceedings of the 2010 ACM Symposium on Applied Computing
Gender classification in uncontrolled settings using additive logistic models

ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Learning local binary patterns for gender classification on real-world face images

Pattern Recognition Letters
Local gradient increasing pattern (LGIP) for facial representation and gender recognition

ICIAR'12 Proceedings of the 9th international conference on Image Analysis and Recognition - Volume Part II
Local descriptors and similarity measures for frontal face recognition: A comparative analysis

Journal of Visual Communication and Image Representation
Gender classification of human face images based on adaptive features and support vector machines

Optical Memory and Neural Networks

Quantified Score

Hi-index	0.02

Visualization

Abstract

This paper presents a novel approach for gender classification by boosting local binary pattern-based classifiers. The face area is scanned with scalable small windows from which Local Binary Pattern (LBP) histograms are obtained to effectively express the local feature of a face image. The Chi square distance between corresponding Local Binary Pattern histograms of sample image and template is used to construct weak classifiers pool. Adaboost algorithm is applied to build the final strong classifiers by selecting and combining the most useful weak classifiers. In addition, two experiments are made for classifying gender based on local binary pattern. The male and female images set are collected from FERET databases. In the first experiment, the features are extracted by LBP histograms from fixed sub windows. The second experiment is tested on our boosting LBP based method. Finally, the results of two experiments show that the features extracted by LBP operator are discriminative for gender classification and our proposed approach achieves better performance of classification than several others methods.