Trunk Branch Ensemble Convolutional Neural Networks for Large Scale, Few Shot Video to Still Face Recognition
For the real world face recognition, factors such as occlusion and pose variant (cross face) would affect the identification/verification performance. In addition, large number of classes also increase the complexity, which makes verification/identification even harder. In order to deal with these issues, how to extract discriminative embeddings is a challenging task for the researchers. This research aims at video to still (V2S) face identification, which means given few images per person (p.p. as our database (also called gallery) we tend to identify if a person in a video is someone in our database or not. We use end to end Trunk Branch Ensemble Convolutional Neural Networks (TBE CNN) combined with state of the art InceptionNet to create informative image patches and boost the features for occlusion and pose variant issues. The images for learning and identifying comprise occlusions, different face directions and also resolution issues. Moreover, we create a large scale, few shot video to still (still to still) face recognition dataset with different settings to evaluate the the models and find the preferred settings for real world face recognition application. How to ensure the accuracy under these practical noises and settings is the goal of this research.