GENDER CLASSIFICATION IN UNCONTROLLED SETTINGS USING ADDITIVE LOGISTIC MODELS.
2009 16TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, VOLS 1-6.
(pp. 2529 - 2532).
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.
|Title:||GENDER CLASSIFICATION IN UNCONTROLLED SETTINGS USING ADDITIVE LOGISTIC MODELS|
|Event:||16th IEEE International Conference on Image Processing|
|Dates:||2009-11-07 - 2009-11-10|
|Keywords:||Gender identification, Boosting|
|UCL classification:||UCL > School of BEAMS > Faculty of Engineering Science > Computer Science|
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