Real-world gender classification via local Gabor binary pattern and three-dimensional face reconstruction by generic elastic model

In this study, a novel method is proposed for gender classification by adding facial depth features to texture features. Accordingly, the three-dimensional (3D) generic elastic model is used to reconstruct the 3D model from human face using only a single 2D frontal image. Then, the texture and depth are extracted from the reconstructed face model. Afterwards, the local Gabor binary pattern (LGBP) is applied to both facial texture and reconstructed depth to extract the feature vectors from both texture and reconstructed depth images. Finally, by combining 2D and 3D feature vectors, the final LGBP histogram bins are generated and classified by the support vector machine. Favourable outcomes are acquired for gender classification on the labelled faces in the wild and FERET databases based on the proposed method compared to several state-of-the-arts in gender classification.