Dataset Restricted Access
Replay-Attack is a dataset for face recognition and presentation attack detection (anti-spoofing). The dataset consists of 1300 video clips of photo and video presentation attack (spoofing attacks) to 50 clients, under different lighting conditions.
Spoofing Attacks Description
The 2D face spoofing attack database consists of 1,300 video clips of photo and video attack attempts of 50 clients, under different lighting conditions.
The data is split into 4 sub-groups comprising:
Clients that appear in one of the data sets (train, devel or test) do not appear in any other set.
All videos are generated by either having a (real) client trying to access a laptop through a built-in webcam or by displaying a photo or a video recording of the same client for at least 9 seconds. The webcam produces colour videos with a resolution of 320 pixels (width) by 240 pixels (height). The movies were recorded on a Macbook laptop using the QuickTime framework (codec: Motion JPEG) and saved into ".mov" files. The frame rate is about 25 Hz. Besides the native support on Apple computers, these files are *easily* readable using mplayer, ffmpeg or any other video utilities available under Linux or MS Windows systems.
Real client accesses as well as data collected for the attacks are taken under two different lighting conditions:
* **controlled**: The office light was turned on, blinds are down, background is homogeneous;
* **adverse**: Blinds up, more complex background, office lights are out.
To produce the attacks, high-resolution photos and videos from each client were taken under the same conditions as in their authentication sessions, using a Canon PowerShot SX150 IS camera, which records both 12.1 Mpixel photographs and 720p high-definition video clips. The way to perform the attacks can be divided into two subsets: the first subset is composed of videos generated using a stand to hold the client biometry ("fixed"). For the second set, the attacker holds the device used for the attack with their own hands. In total, 20 attack videos were registered for each client, 10 for each of the attacking modes just described:
4 x mobile attacks using an iPhone 3GS screen (with resolution 480x320 pixels) displaying:
4 x high-resolution screen attacks using an iPad (first generation, with a screen resolution of 1024x768 pixels) displaying:
2 x hard-copy print attacks (produced on a Triumph-Adler DCC 2520 color laser printer) occupying the whole available printing surface on A4 paper for the following samples:
The 1300 real-accesses and attacks videos were then divided in the following way:
We also provide face locations automatically annotated by a cascade of classifiers based on a variant of Local Binary Patterns (LBP) referred as Modified Census Transform (MCT) [Face Detection with the Modified Census Transform, Froba, B. and Ernst, A., 2004, IEEE International Conference on Automatic Face and Gesture Recognition, pp. 91-96]. The automatic face localisation procedure works in more than 99% of the total number of frames acquired. This means that less than 1% of the total set of frames for all videos do not possess annotated faces. User algorithms must account for this fact.
Protocol for Licit Biometric Transactions
It is possible to measure the performance of baseline face recognition systems on the 2D Face spoofing database and evaluate how well the attacks pass such systems or how, otherwise robust they are to attacks. Here we describe how to use the available data at the enrolment set to create a background model, client models and how to perform scoring using the available data.
Protocols for Spoofing Attacks
Attack protocols are used to evaluate the (binary classification) performance of counter-measures to spoof attacks. The database can be split into 6 different protocols according to the type of device used to generate the attack: print, mobile (phone), high-definition (tablet), photo, video or grand test (all types). Furthermore, subsetting can be achieved on the top of the previous 6 groups by classifying attacks as performed by the attacker bare hands or using a fixed support. This classification scheme makes-up a total of 18 protocols that can be used for studying the performance of counter-measures to 2D face spoofing attacks. The table bellow details the amount of video clips in each protocol.
If you use this database, please cite the following publication:
I. Chingovska, A. Anjos, S. Marcel,"On the Effectiveness of Local Binary Patterns in Face Anti-spoofing"; IEEE BIOSIG, 2012.
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