SiW-Enroll

SiW (Spoofing in the Wild) is a face anti-spoofing dataset recently introduced in [29] where images are extracted from short videos captured at high resolution and 30 frames per second. In total, 4,478 videos are collected from 165 subjects including variations in spoof type, recording device, illumination condition, pose and facial expression.

To define train and test sets, we start by following the splitting system described as Protocol 1 in [29]. We further subsample train and test sets by extracting 1 every 10 frames (or every 0.33 seconds) in each video, since consecutive frames are almost identical. Finally, since we observe that simple models can reach very high accuracy, we further make the task harder by creating 2 separate training and evaluation folds in a way that the model is trained and tested on different spoof types. Since the original dataset is collected using two different capturing devices, we can use this information for the definition of enrollment sets. Indeed, in a real scenario, the subject would have to enroll twice when using two different devices, meaning that a unique enrollment set will be generated for each combination of subject and capturing devices. Capturing the sensor bias in the enrollment can be particularly beneficial, for example to detect if the resolution of a face changes, which can indicate a replay attack. To extract enrollment data we choose a single video from each subject and sensor among the available ones. We arbitrarily pick the video without illumination changes and with variation in subject pose, as it resembles the enrollment conditions required in real devices. We then equidistantly sample N frames over the video to construct the enrollment set. As shown in Fig. 2, this allows capturing different poses and facial expressions from the subject. Finally, we exclude all the frames in this video from training and test data to avoid in341 formation leakage and match all the remaining queries from the same subject and sensor to their unique enrollment set.

(See paper for additional details)