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Robust face recognition is the task of performing recognition in an unconstrained environment, where there is variation of view-point, scale, pose, illumination and expression of the face images.
<span style="color:grey; opacity: 0.6">( Image credit: MeGlass dataset )</span>
출처에 같은 태스크 이름으로 연결된 데이터셋입니다. 동명 태스크가 포함될 수 있으므로, 비교할 때는 평가 결과의 조건도 확인하세요.
The MALF dataset is a large dataset with 5,250 images annotated with multiple facial attributes and it is specifically constructed for fine grained evaluation. Source: Pushing the Limits of Unconstrained Face Detection:a Challenge Dataset and Baseline Results Image Source: http://www.cbsr.ia.ac.cn/faceevaluation/
Unconstrained Face Detection Dataset (UFDD) aims to fuel further research in unconstrained face detection. Source: Pushing the Limits of Unconstrained Face Detection: a Challenge Dataset and Baseline Results Image Source: https://ufdd.info/
MeGlass is an eyeglass dataset originally designed for eyeglass face recognition evaluation. All the face images are selected and cleaned from MegaFace. Each identity has at least two face images with eyeglass and two face images without eyeglass. It contains 47,817 images from 1,710 different identities. Source: https://github.com/cleardusk/MeGlass Image Source: https://github.com/cleardusk/MeGlass
Drone Surveillance of Faces, is a large-scale drone dataset intended to facilitate research for face recognition using drones. Data has been captured at two outdoor locations: (i) at the ground level, where subjects are asked to walk in a park-like environment, and (ii) at the terrace of a building. For each location and surveillance scenario, data is captured twice: (i) during the morning and (ii) during the evening, before sunset. For each combination of location, surveillance use case, and time of capture, there exist 25 videos featuring 58 subjects. Each subject belongs to the age bracket of (18, 40) years, and each video contains subjects appearing in groups of 2-3. For a particular setting combination, one group appears in only one video. Since there exist eight combinations of location, surveillance scenario, and time of capture, each group of subjects occur in eight videos, thus resulting in a total of 200 videos. The subjects and pairings remain consistent across different settings. The directory structure represents the combination of location, use case and time of capture.
A occluded version of the LFW dataset for occluded face recognition verification. Uses structured occlusions generated to seem more realistic.
We introduce an annotated dataset of five thousand human labeled pareidolic face images, called ``Faces in Things''. Faces in Things is derived from the LAION-5B dataset and annotated for key face attributes and bounding boxes
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