MCXFace
Description
MCXFace is a heterogeneous face recognition dataset consisting of multi-channel image samples for 51 subjects. For each subject color (RGB), thermal, near-infrared (850 nm), short-wave infrared (1300 nm), Depth, Stereo depth, and depth estimated from RGB images are available. Overall 7406 images together with landmark annotations and standard protocols are available in this dataset.
The Multi-Channel Heterogeneous Face Recognition dataset (MCXFace) is derived from the HQ-WMCA dataset (https://www.idiap.ch/en/dataset/hq-wmca). The MCXFace dataset contains images of 51 subjects collected in different channels under three different sessions and various illumination conditions. The channels available are color (RGB), thermal, near-infrared (850 nm), short-wave infrared (1300 nm), Depth, Stereo depth, and depth estimated from RGB images using the 3DDFA method. All the channels are registered spatially and temporally across all the modalities. In the MCXFace dataset, only bonafide samples are present. Further, the files are divided into train and dev sets with a disjoint set of identities to make experiments in different homogeneous and heterogeneous settings possible. For each of the protocols, we have created five different folds, by randomly dividing the subjects into train and dev partitions. In addition to the images, annotations for left and right eye centers for all the images are also provided in a ``JSON'' file. Specifically for each image, annotations for the left eye, right eye, mouth left corner, mouth right corner, nose, top left, and top right are provided. Each image file is stored as a ``.jpg'' file with a resolution of 1920x1200.
An example set of images for one subject with different modalities is shown below.
Reference
The MCXFace dataset was originally introduced in the following paper:
@ARTICLE{George_IEEETIFS_2022,
author = {George, Anjith and Mohammadi, Amir and Marcel, S{\'{e}}bastien},
projects = {Idiap, HARDENING},
title = {Prepended Domain Transformer: Heterogeneous Face Recognition without Bells and Whistles},
journal = {IEEE Transactions on Information Forensics and Security},
year = {2022},
pdf = {https://publications.idiap.ch/attachments/papers/2022/George_IEEETIFS_2022.pdf}
}
If you use the MCXFace dataset, consider citing the following references as well.
The original HQ-WMCA database is produced at Idiap within the framework of "IARPA BATL" project and it is intended for research, development, and testing in biometrics and biomedical analysis.
The paper which introduced the HQ-WMCA dataset is:
@ARTICLE{Heusch_TBIOM_2020,
author = {Heusch, Guillaume and George, Anjith and Geissb{\"u}hler, David and Mostaani, Zohreh and Marcel, S{\'{e}}bastien},
title = {Deep Models and Shortwave Infrared Information to Detect Face Presentation Attacks},
journal = {IEEE Transactions on Biometrics, Behavior, and Identity Science},
year = {2020},
publisher = {IEEE},
url = {http://publications.idiap.ch/downloads/papers/2020/Heusch_TBIOM_2020.pdf}
}
More details about the HQ-WMCA data can be found in the following research report.
@TECHREPORT{Mostaani_Idiap-RR-22-2020,
author = {Mostaani, Zohreh and George, Anjith and Heusch, Guillaume and Geissenbuhler, David and Marcel, S{\'{e}}bastien},
projects = {Idiap, ODIN/BATL},
month = {9},
title = {The High-Quality Wide Multi-Channel Attack (HQ-WMCA) database},
type = {Idiap-RR},
number = {Idiap-RR-22-2020},
year = {2020},
institution = {Idiap},
pdf = {https://publidiap.idiap.ch/downloads//reports/2020/Mostaani_Idiap-RR-22-2020.pdf}
}