Boosting sex identification performance

Authors:
Shumeet Baluja;Henry Rowley
Affiliations:
Google, Inc. and Carnegie Mellon University, Computer Science Department;Google, Inc.
Venue:
IAAI'05 Proceedings of the 17th conference on Innovative applications of artificial intelligence - Volume 3
Year:
2005

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Abstract

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.