Boosting Sex Identification Performance

  • Authors:
  • Shumeet Baluja;Henry A. Rowley

  • Affiliations:
  • Google Research, Google Inc., Mountain View 94043;Google Research, Google Inc., Mountain View 94043

  • Venue:
  • International Journal of Computer Vision
  • Year:
  • 2007

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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脳 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.