UCL logo

UCL Discovery

UCL home » Library Services » Electronic resources » UCL Discovery

GENDER CLASSIFICATION IN UNCONTROLLED SETTINGS USING ADDITIVE LOGISTIC MODELS

Prince, SJD; Aghajanian, J; (2009) GENDER CLASSIFICATION IN UNCONTROLLED SETTINGS USING ADDITIVE LOGISTIC MODELS. In: 2009 16TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, VOLS 1-6. (pp. 2529 - 2532). IEEE

Full text not available from this repository.

Abstract

Many previous studies have investigated gender classification in well-lit frontal images. In this paper we consider images where the pose, expression and lighting are relatively unconstrained. We localize faces using a standard sliding-window detector. We preprocess the facial region by convolving with Gabor filters at at four scales and four orientations. We sample these responses and concatenate them to form a feature vector. We develop a classifier based on an additive sum of non-linear functions of one-dimensional projections of the data. In particular we investigate arc tangent and weighted sums of Gaussians. We describe a training method based on increasing the binomial log likelihood. We demonstrate that our system on two databases and show that it performs well relative to the state of the art.

Type:Proceedings paper
Title:GENDER CLASSIFICATION IN UNCONTROLLED SETTINGS USING ADDITIVE LOGISTIC MODELS
Event:16th IEEE International Conference on Image Processing
Location:Cairo, EGYPT
Dates:2009-11-07 - 2009-11-10
ISBN-13:978-1-4244-5653-6
Keywords:Gender identification, Boosting
UCL classification:UCL > School of BEAMS > Faculty of Engineering Science > Computer Science

Archive Staff Only: edit this record