  It is unknown what kind of biases modern in the wild face datasets have
because of their lack of annotation. A direct consequence of this is that total
recognition rates alone only provide limited insight about the generalization
ability of a Deep Convolutional Neural Networks (DCNNs). We propose to
empirically study the effect of different types of dataset biases on the
generalization ability of DCNNs. Using synthetically generated face images, we
study the face recognition rate as a function of interpretable parameters such
as face pose and light. The proposed method allows valuable details about the
generalization performance of different DCNN architectures to be observed and
compared. In our experiments, we find that: 1) Indeed, dataset bias has a
significant influence on the generalization performance of DCNNs. 2) DCNNs can
generalize surprisingly well to unseen illumination conditions and large
sampling gaps in the pose variation. 3) Using the presented methodology we
reveal that the VGG-16 architecture outperforms the AlexNet architecture at
face recognition tasks because it can much better generalize to unseen face
poses, although it has significantly more parameters. 4) We uncover a main
limitation of current DCNN architectures, which is the difficulty to generalize
when different identities to not share the same pose variation. 5) We
demonstrate that our findings on synthetic data also apply when learning from
real-world data. Our face image generator is publicly available to enable the
community to benchmark other DCNN architectures.
