CelebA-Spoof is a large-scale face anti-spoofing dataset recently introduced in [53]. The dataset contains 625,537 images of 10,177 celebrities captured under different spoof mediums, environments and illumination conditions. The original dataset proposes three different evaluation protocols. For our experimentation, we focus on the most general ”intra” protocol, in which different spoof types, environments and illumination conditions are used for both training and testing.

To generate CelebA-Spoof-Enroll, the personalized version of the CelebA-Spoof anti-spoofing dataset, we start by setting the desired enrollment set size N. We decide for a constant number of enrollment images per user to be consistent with the implementation in typical commercial applications and to simplify the dataset definition. For both the training and test split, we count the number of live samples per user and discard those users having ≤ N live samples. So, if we desire a higher value of N, a larger number of users would get rejected and hence the number of training samples would be less. Note that it is not possible to include all original data, as a number of users in CelebA-Spoof are missing live samples. Nevertheless, only a very small percentage of training and test data is discarded through this process when choosing N < 10. For each accepted user, the first N live samples (ordered according to the ascending alphanumeric ordering of the original filenames) are chosen to define its enrollment set. The rest of the live samples and the spoof samples are marked as query samples. It is important to note that using this deterministic method of obtaining enrollment samples does not introduce unwanted bias as most of the CelebASpoof images are randomly crawled from the internet and are not ordered according to specific criteria. With this setting, we associate a user identifier with each query sample such that queries can be easily mapped to the correct enrollment set. This method of filtering users for a desired enrollment size N is performed for both training and test split. In the rest of the paper, we refer to CelebA-Spoof-EnrollN or in short CASp-EnrollN to describe the personalized version of the dataset with N enrollment images

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