Boosting Sex Identification Performance

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Boosting Sex Identification Performance
This paper presents a method based on AdaBoost to identify the sex of a person from a low resolution grayscale picture of their face. The method described here is implemented in a system that will process well over 109 images. The goal of this work is to create an efficient system that is both simple to implement and maintain; the methods described here are extremely fast and have straightforward implementations. We achieve 80% accuracy in sex identification with less than 10 pixel comparisons and 90% accuracy with less than 50 pixel comparisons. The best classifiers published to date use Support Vector Machines; we match their accuracies with as few as 500 comparison operations on a 20×20 pixel image. The AdaBoost based classifiers presented here achieve over 93% accuracy; these match or surpass the accuracies of the SVM-based classifiers, and yield performance that is 50 times faster.
Shumeet Baluja, Henry A. Rowley
Added 15 Dec 2010
Updated 15 Dec 2010
Type Journal
Year 2007
Where IJCV
Authors Shumeet Baluja, Henry A. Rowley
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