The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Gender with Support Faces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Real-Time Face Detection
International Journal of Computer Vision
Boosting Sex Identification Performance
International Journal of Computer Vision
Evaluation of Gender Classification Methods with Automatically Detected and Aligned Faces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Gender Classification on Consumer Images in a Multiethnic Environment
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
Revisiting Linear Discriminant Techniques in Gender Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Random k-Labelsets for Multilabel Classification
IEEE Transactions on Knowledge and Data Engineering
Gait Components and Their Application to Gender Recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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Estimating human face demography from images is a problem that has recently been extensively studied because of its relevant applications. We review state-of-the-art approaches to gender classification and confirm that their performance drops significantly when classifying young or elderly faces. We hypothesize that this is caused by the existence of dependencies among the demographic variables that were not considered in traditional gender classifiers. In the paper we confirm experimentally the existence of such dependencies between age and gender variables. We also prove that the performance of gender classifiers can be improved by considering the dependencies with age in a multi-dimensional approach. The performance improvement is most prominent for young and elderly faces.