DF-Platter is a novel DeepFake dataset comprising 133,260 videos generated using three different generation techniques. It is one of the first large-scale datasets which incorporates the concept of Multi-subject deepfakes which implies having more than one deepfake subject in one frame of a video. The database can be downloaded from here.
DeePhy is a novel DeepFake Phylogeny dataset consisting of 5040 DeepFake videos generated using three different generation techniques. It is one of the first datasets which incorporates the concept of Deepfake Phylogeny which refers to the idea of generation of DeepFakes using multiple generation techniques in a sequential manner. The database can be downloaded from here.
The database can be downloaded from here.
The database can be downloaded from here.
The database can be downloaded from here.
The database can be downloaded from here.
The database can be downloaded from here.
The DFW dataset consists of 1000 subjects and 11157 images. Pre-defined training and testing splits are provided, where 400 subjects form the training set, while the remaining 600 subjects form the test set. Each subject may contain normal, validation, disguised, and impersonator images.
The database can be downloaded from here.
The KIVI database consists of video sequences of 503 individuals forming a total of 355 positive kin pairs from 211 families. For creating the proposed KIVI database, popular celebrity families are selected.
Further details here.
This database contains videos captured both daytime and nighttime where the subject is at a standoff distance of 1-10 mts from the camera. It can be used to perform experiments for cross-spectral and cross resolution face recognition.
Further details here.
Multispectral Latex Mask based Video Face Presentation Attack (MLFP) is collected by Indraprastha Institute of Information Technology-Delhi (IIIT-D) and West Virgina University-USA (WVU-USA). The prime focus of the database is the development of face presentation attack detection algorithms in multiple spectrum. The database is publicly available only for research purpose.
The database can be downloaded from here.
VIS+NIR Print Attack Database is coming soon...
IIIT-D Aging Database is coming soon...
Illicit Drug Abuse Face Database is coming soon...
Silicone Mask Attack Database (SMAD) consists of 65 videos of face biometric presentation attacks through real life silicone masks and 65 genuine face biometric access videos. The database is assembled to facilitate research efforts in building algorithms for unconstrained face presentation attack detection
The list of URLs is compiled in a text file.
Disclaimer: The videos in the Silicone Mask Face Attack Database are downloaded from the internet. The time interval of spoof and genuine videos varies from 3 to 15 second and may differ from the original videos. Therefore, we are willing to share videos and their labels as used in our experiments. Please contact via e-mail for the same.
The dataset contains 464 skull images, and is divided into two parts: (i) skull and digital face image pairs, and (ii) unlabeled supplementary skull images. The first component consists of pairs collected from real world examples of solved skull identification cases, while the second part contains unlabeled skull images.
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The existing PRIP database is extended by adding another set by a user belonging to Indian ethnicity using the software FACES. This version is called as the extended PRIP or the E-PRIP database.
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IIIT-Delhi Disguise Face Database is a dataset containing images pertaining to 75 subjects with different kinds of disguise variations. The version 1 of the dataset consists of images captured in visible spectrum. It is named as IIIT-Delhi Disguise Version 1 face database (ID V1). The IIITD In and Beyond Visible Spectrum Disguise database (I2BVSD)consists of face images captured in visible as well as thermal spectrum. Thus, ID V1 is a subset of I2BVSD.
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The IIIT-D RGB-D face database comprises of 106 male and female subjects with multiple RGB-D images of each subject. All the images are captured using a Microsoft Kinect sensor. The OpenNI API captures the RGB image and depth map as separate 24-bit images. Since the images are unsegmented, the database can be used for both face detection and recognition in RGB-D space. The number of images per subject are variable with a minimum of 11 images and a maximum of 254. The total number of images in the database is 4605 and the size of each image is 640x480.
For more information please visit this page.
IIIT-Delhi WhoIsIt Database (WIT) has been superseded by IIIT-Delhi extendedWhoIsIt Database (eWIT).
IIIT-Delhi extendedWhoIsIt Database (eWIT) contains images of 200 well known personalities, with at least 10 images per subject. The images aim to capture the age and weight variations of the subjects over a period of time. The average age span per subject is 28.78 years. All the images have been downloaded from the internet and age has been estimated by the authors. The details of the database and performance of some well known face recognition algorithms is available in the papers mentioned below.
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IIIT-Delhi sketch database consist of three different types of sketches, namely, viewed, semi-forensic, and forensic sketches. Each user in the database has a sketch and a corresponding digital photograph. It introduces semi-forensic sketches that are drawn based on the memory of sketch artist to act as an intermediate representation between viewed and forensic sketches.
For more information please visit this page.
Disclaimer: This database consists of images collected from the internet. These forensic sketch-digital image pairs are collected in same spirit and understanding as the PubFig dataset and the Labeled Faces in the Wild (LFW) dataset. Therefore, we are sharing the direct link to the face images. Researchers can use this database but are not encouraged to publish any images from the database due to privacy reasons. Copyright of these images are with the original creators.
The database can be downloaded from the following link.
Look Alike face database consists of images pertaining to 50 well known personalities (from western, eastern, and asian origins) and their look-alikes. Each subject/class has five genuine images (total 50 x 5 genuine cases) and five look-alike images (total 50 x 5 look-alikes). To obtain the database, email the duly filled license agreement, to databases@iab-rubric.org with the subject line "License agreement for Look Alike Face Database".
Database can be downloaded from here.
NOTE: The license agreement has to be signed by someone having the legal authority to sign on behalf of the institute, such as the head of the institution or registrar. If a license agreement is signed by someone else, it will not be processed further.
This database is available only for research and educational purpose and not for any commercial use. If you use the database in any publications or reports, you must refer to the following paper:
The plastic surgery face database is a real world database that contains 1800 pre and post surgery images pertaining to 900 subjects. For each individual, there are two frontal face images with proper illumination and neutral expression: the first is taken before surgery and the second is taken after surgery. The database contains 519 image pairs corresponding to local surgeries and 381 cases of global surgery (e.g., skin peeling and face lift). The details of the database and performance evaluation of several well known face recognition algorithms is available in the paper mentioned below.
The list of URLs is compiled in a text file along with a tool to download the images present at these URLs. The tool will download the images and store them at the specified location.
Disclaimer: The images in the plastic surgery database are downloaded from the internet and some of the subjects appear on different websites under different surgery labels. Therefore, this database may have some repetition of subjects across different types of surgeries. We have figured out multiple cases with such inconsistencies and provided an errata. If you come across some other cases as well, kindly report it to us.
Please find the text file of errata. The images seperated by comma(,) represent the redundant entries in the database.