LightFace: A Hybrid Deep Face Recognition Framework
By Sefik Ilkin Serengil, Alper Ozpinar. 2020
401 citations recorded by the source corpus.
Face recognition constitutes a relatively a popular area which has emerged from the rulers of the social media to top universities in the world. Those frontiers and rule makers recently designed deep learning based custom face recognition models. A modern face recognition pipeline consists of four common stages: detecting, alignment, representation and verification. However, face recognition studies mainly mention the representation stage of a pipeline. In this paper, first of all a review face recognition has been done and then the description of the developed lightweight hybrid high performance face recognition framework has been made. Its hybrid feature enables to switch face recognition models among state-of-the-art ones.
Subfields: Computer Vision and Pattern Recognition, Signal Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Luc Van Gool
- Dacheng Tao
- Jitendra Malik
- Trevor Darrell
- Xiaogang Wang
- Andrea Vedaldi
- Ming-Hsuan Yang
- Andrew Zisserman
In Signal Processing
- Albert Gordo
- Ross B. Girshick
- Piotr Doll'ar
- Alexander C. Berg
- Laura Gustafson
- Brian Dolhansky
- Xiaoou Tang
- Xiaogang Wang
Citations in the corpus
- Segment Anything (2023, methodology): “We used the RetinaFace [88, 89] model (https://github.com/serengil/retinaface) to detect faces.”
- Towards Measuring Fairness in AI: The Casual Conversations Dataset (2021, background): “LightFace [23], on the other hand, is more”