Empirically analyzing the effect of dataset biases on deep face recognition systems
Kortylewski A, Egger B, Schneider A, Gerig T, Morel-Forster A, Vetter T (2018)
Publication Type: Conference contribution
Publication year: 2018
Publisher: IEEE Computer Society
Book Volume: 2018-June
Pages Range: 2174-2183
Conference Proceedings Title: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Event location: Salt Lake City, UT
ISBN: 9781538661000
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.
Authors with CRIS profile
Involved external institutions
Switzerland (CH)How to cite
APA:
Kortylewski, A., Egger, B., Schneider, A., Gerig, T., Morel-Forster, A., & Vetter, T. (2018). Empirically analyzing the effect of dataset biases on deep face recognition systems. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (pp. 2174-2183). Salt Lake City, UT, US: IEEE Computer Society.
MLA:
Kortylewski, Adam, et al. "Empirically analyzing the effect of dataset biases on deep face recognition systems." Proceedings of the 31st Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2018, Salt Lake City, UT IEEE Computer Society, 2018. 2174-2183.
BibTeX: Download