For masking attack, masks with and without cropping are considered. For video replay attack, we display a face video on Lenovo LCD screen and Mac screen. For printed paper attack, face image with still printed paper and quivering printed paper (A4 size) are used. We consider three spoofing attack types including printed paper attack, video replay attack, and masking attack. If the client wears eye-glasses, there will be another 25 videos. The scene covers 5 different illumination conditions in office environment. The standoff distance between face and camera is about 30-50 cm.įor genuine face video, normally there are 25 videos (5 devices with 5 scenes). All face videos are captured by a front-facing camera. Five mobile phones were used to collect the database: (a) Hasee smart-phone (with resolution of 640 * 480), (b) Huawei Smart-phone (with resolution of 640 * 480), (c) iPad 4 (with resolution of 640 * 480), (d) iPhone 5s (with resolution of 1280 * 720) and (e) ZTE smart-phone (with resolution of 1280 * 720). It also includes a new Client-Specific One-Class Domain Adaptation Protocol with an additional 1.25GB of pre-processed data.įor each subject, there are 150-200 video clips with the average duration around 10 seconds. The ROSE-Youtu Face Liveness Detection Database (ROSE-Youtu) consists of 4225 videos with 25 subjects in total (3350 videos with 20 subjects publically available with 5.45GB in size). We introduce a new and comprehensive face anti-spoofing database, ROSE-Youtu Face Liveness Detection Database, which covers a large variety of illumination conditions, camera models, and attack types. ROSE-Youtu Face Liveness Detection Dataset
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