Empirically Analyzing the Effect of Dataset Biases on Deep Face Recognition Systems

TL;DR

This paper empirically investigates how dataset biases affect the generalization of deep face recognition systems, revealing key insights into architecture performance and limitations using synthetic and real data.

Contribution

It introduces a methodology using synthetic face images to analyze the impact of dataset biases on DCNN generalization, providing new insights into architecture performance.

Findings

Dataset bias significantly influences DCNN generalization.

DCNNs generalize well to unseen illumination and pose gaps.

VGG-16 outperforms AlexNet in pose generalization.

Abstract

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.