Face and gestures refers to the combined recognition and interpretation of both facial expressions and physical gestures as a means of understanding human communication and interaction. The integration of face and gestures in a deep learning model can find applications in human-computer interaction, virtual reality, augmented reality, or other domains where understanding both facial expressions and physical gestures is important for communication and interaction.


Learning to Learn across Diverse Data Biases in Deep Face Recognition

Convolutional Neural Networks have achieved remarkable success in face recognition, in part due to the abundant availability of data. However, the data used for training CNNs is often imbalanced. Prior works largely focus on the long-tailed nature of face datasets in data volume per identity or focus on single bias variation. In this paper, we show that many bias variations such as ethnicity, head pose, occlusion and blur can jointly affect the accuracy significantly. We propose a sample level weighting approach termed Multi-variation Cosine Margin (MvCoM), to simultaneously consider the multiple variation factors, which orthogonally enhances the face recognition losses to incorporate the importance of training samples. Further, we leverage a learning to learn approach, guided by a held-out meta learning set and use an additive modeling to predict the MvCoM. Extensive experiments on challenging face recognition benchmarks demonstrate the advantages of our method in jointly handling imbalances due to multiple variations.