Datasets

• Body-Worn Camera based Open-set Face Recognition (BWCFace)

  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).

  

  Identities

  BWCFace contains face images from 132+ subjects.

  Ethntic Groups

  BWCFace contains face images from 132+ subjects.

  Face Samples

  Thousands of frames are extracted per each identity.

  Paper to be Cited:

  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.

  Citation in BibTeX:

  @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

• Gender Balanced Deep Fake Dataset (GBDF)

  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)

  Github: Please follow the link to access the dataset Dataset link

  
  Paper to be Cited:

  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.

  Citation in BibTeX:

  @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

Code

Ocular-based user recognition project page is (Here) and the code for all the publications for ocular-based user recognition are available at (GitHub).