Heterogeneous Face Recognition

Contains over 11000 images of 1000 identities with different types of disguise accessories. The dataset is collected from the Internet, resulting in unconstrained face images similar to real world settings. Source: Recognizing Disguised Faces in the Wild

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.