Flickr-PAD: New Face High-Resolution Presentation Attack Detection Database
This provides a more diverse and realistic database for researchers in biometric security, though it is incremental as it focuses on data rather than method innovation.
The authors tackled the lack of high-quality, realistic face presentation attack detection (PAD) databases by creating Flickr-PAD, a new database based on open-access Flickr images with high-resolution printed and screen scenarios, achieving a best result of 7.08% BPCER10 and 11.15% BPCER20 using MobileNet-V3 large.
Nowadays, Presentation Attack Detection is a very active research area. Several databases are constituted in the state-of-the-art using images extracted from videos. One of the main problems identified is that many databases present a low-quality, small image size and do not represent an operational scenario in a real remote biometric system. Currently, these images are captured from smartphones with high-quality and bigger resolutions. In order to increase the diversity of image quality, this work presents a new PAD database based on open-access Flickr images called: "Flickr-PAD". Our new hand-made database shows high-quality printed and screen scenarios. This will help researchers to compare new approaches to existing algorithms on a wider database. This database will be available for other researchers. A leave-one-out protocol was used to train and evaluate three PAD models based on MobileNet-V3 (small and large) and EfficientNet-B0. The best result was reached with MobileNet-V3 large with BPCER10 of 7.08% and BPCER20 of 11.15%.