Learning local binary patterns for gender classification on real-world face images

Gender recognition is one of fundamental face analysis tasks. Most of the existing studies have focused on face images acquired under controlled conditions. However, real-world applications require gender classification on real-life faces, which is much more challenging due to significant appearance variations in unconstrained scenarios. In this paper, we investigate gender recognition on real-life faces using the recently built database, the Labeled Faces in the Wild (LFW). Local Binary Patterns (LBP) is employed to describe faces, and Adaboost is used to select the discriminative LBP features. We obtain the performance of 94.81% by applying Support Vector Machine (SVM) with the boosted LBP features. The public database used in this study makes future benchmark and evaluation possible.

► We investigate gender recognition on real-life faces.
► We use the Labeled Faces in the Wild database in our study.
► Discriminative LBP features are learned to describe faces.
► The performance of 94.81% is obtained by applying SVM with the learned features.