O
Omkar M. Parkhi
Researcher at University of Oxford
Publications - 28
Citations - 10082
Omkar M. Parkhi is an academic researcher from University of Oxford. The author has contributed to research in topics: Facial recognition system & TRECVID. The author has an hindex of 16, co-authored 25 publications receiving 7648 citations. Previous affiliations of Omkar M. Parkhi include Facebook & International Institute of Information Technology, Hyderabad.
Papers
More filters
Proceedings ArticleDOI
Deep face recognition
TL;DR: It is shown how a very large scale dataset can be assembled by a combination of automation and human in the loop, and the trade off between data purity and time is discussed.
Proceedings ArticleDOI
VGGFace2: A Dataset for Recognising Faces across Pose and Age
TL;DR: VGGFace2 as discussed by the authors is a large-scale face dataset with 3.31 million images of 9131 subjects, with an average of 362.6 images for each subject.
Proceedings ArticleDOI
Cats and dogs
TL;DR: These models are very good: they beat all previously published results on the challenging ASIRRA test (cat vs dog discrimination) when applied to the task of discriminating the 37 different breeds of pets, and obtain an average accuracy of about 59%, a very encouraging result considering the difficulty of the problem.
Proceedings ArticleDOI
Fisher Vector Faces in the Wild.
TL;DR: This paper shows that Fisher vectors on densely sampled SIFT features are capable of achieving state-of-the-art face verification performance on the challenging “Labeled Faces in the Wild” benchmark, and shows that a compact descriptor can be learnt from them using discriminative metric learning.
Proceedings ArticleDOI
The truth about cats and dogs
TL;DR: This approach proposes to use the template-based model to detect a distinctive part for the class, followed by detecting the rest of the object via segmentation on image specific information learnt from that part, and achieves accuracy comparable to the state-of-the-art on the PASCAL VOC competition, which includes other models such as bag- of-words.