Enhancing gender classification by combining 3D and 2D face modalities

Shape and texture provide different modalities in face-based gender classification. While in the literature, the majority of works deal with shape or texture modality individually, only a few concern their combination. And to the best of our knowledge, no work considers the combination with the 3D meshes. Thus in this work, we combine shape and texture modalities for gender classification, with both the combination of range images and gray images, and the combination of 3D meshes and gray images. In 10-fold subject-independent cross-validation with Random Forest on the FRGC-2.0 dataset, we achieved a correctness of 93.27% ± 5.16%, which outperforms each individual modality and is comparable to the state-of-the-art. Results confirm that shape and texture modalities are complementary, and their combination enhances the performance of face-based gender classification.