BWCFace is a large-scale face recognition dataset. Face images are collected from a body-worn camera mounted at the chest level. The analysis and benchmarks results can be found in the paper “BWCFace: Open-set Face Recognition using Body-worn Camera” (BWCFace).
BWCFace contains face images from 132+ subjects.
BWCFace contains face images from 132+ subjects.
Thousands of frames are extracted per each identity.
Ali Almadan, Anoop Krishnan, and Ajita Rattani, “BWCFace: Open-set Face Recognition using Body-worn Camera”, 19th IEEE International Conference On Machine Learning And Applications (ICMLA), Florida, USA, pp. 1036-1043, 2020.
@article{almadan2020bwcface, title={BWCFace: Open-set Face Recognition using Body-worn Camera}, author={Almadan, Ali and Krishnan, Anoop and Rattani, Ajita}, journal={arXiv preprint arXiv:2009.11458}, year={2020} }
**Please contact the authors to request the dataset: aaalmadan[at]shockers.wichita.edu or ajita.rattani[at]wichita.edu
GBDF is a publicly available gender-balanced annotated deepfake dataset created from FaceForensics++ (FF++), Celeb-DF, and Deeper Forensics-1.0 consisting of 10,000 live and fake videos generated using different identity and expression swapping deepfake generation techniques. The dataset consist of 10,000 videos with 5000 each for males and females with 1:4 real to fake ratio.(GBDF)
Aakash Varma Nadimpalli and Ajita Rattani, "GBDF: Gender Balanced DeepFake Dataset Towards Fair DeepFake Detection", ICPR Workshop on MultiMedia FORensics in the WILD, International Conference on Pattern Recognition, Montreal, Québec, pp. 320-337, 2022.
@article{varma2022gbdf,title={GBDF: Gender Balanced DeepFake Dataset Towards FairDeepFake Detection}, author={Varma Nadimpalli, Aakash and Rattani, Ajita},journal={arXiv e-prints},pages={arXiv--2207},year={2022}}
**Please contact the authors to request the dataset: axnadimpalli[at]shockers.wichita.edu or ajita.rattani[at]wichita.edu