Robust gender recognition by exploiting facial attributes dependencies

• Robust gender recognition using dependencies among demographic attributes.
• By learning pose we also avoid on-line face alignment.
• We use dependencies of gender with eyes and head pose to improve estimation.
• The dependencies can be exploited by an efficient procedure based on LDA.

Estimating human face gender from images is a problem that has been extensively studied because of its relevant applications. Recent works report significant drops in performance for state-of-the-art gender classifiers when evaluated “in the wild,” i.e., with uncontrolled demography and environmental conditions. We hypothesize that this is caused by the existence of dependencies among facial demographic attributes that have not been considered when building the classifier. In the paper we study the dependencies among gender, age and pose facial attributes. By considering the relation between gender and pose attributes we also avoid the use of computationally expensive and fragile face alignment procedures. In the experiments we confirm the existence of dependencies among gender, age and pose facial attributes and prove that we can improve the performance and robustness of gender classifiers by exploiting these dependencies.