  A face spoofing attack occurs when an intruder attempts to impersonate
someone who carries a gainful authentication clearance. It is a trending topic
due to the increasing demand for biometric authentication on mobile devices,
high-security areas, among others. This work introduces a new database named
Sense Wax Attack dataset (SWAX), comprised of real human and wax figure images
and videos that endorse the problem of face spoofing detection. The dataset
consists of more than 1800 face images and 110 videos of 55 people/waxworks,
arranged in training, validation and test sets with a large range in
expression, illumination and pose variations. Experiments performed with
baseline methods show that despite the progress in recent years, advanced
spoofing methods are still vulnerable to high-quality violation attempts.
