CL-FedFR: Curriculum learning for federated face recognition
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Type :
Conference paper
Publication Status :
Published
Access :
Attribution-NonCommercial-NoDerivatives 4.0 International
openAccess
http://creativecommons.org/licenses/by-nc-nd/4.0/
openAccess
http://creativecommons.org/licenses/by-nc-nd/4.0/
Abstract
Face recognition (FR) has been significantly enhanced by the advent and continuous improvement of deep
learning algorithms and accessibility of large datasets. However, privacy concerns raised by using and distributing
face image datasets have emerged as a significant barrier to the deployment of centralized machine
learning algorithms. Recently, federated learning (FL) has gained popularity since the private data at edge
devices (clients) does not need to be shared to train a model. FL also continues to drive FR research toward
decentralization. In this paper, we propose novel data-based and client-based curriculum learning (CL) approaches
for federated FR intending to improve the performance of generic and client-specific personalized
models. The data-based curriculum utilizes head pose angles as the difficulty measure and feeds the images
from “easy” to “difficult” during training, which resembles the way humans learn. Client-based curriculum
chooses “easy clients” based on performance during the initial rounds of training and includes more “difficult
clients” at later rounds. To the best of our knowledge, this is the first paper to explore CL for FR in a FL
setting. We evaluate the proposed algorithm on MS-Celeb-1M and IJB-C datasets and the results show an
improved performance when CL is utilized during training.
Source :
Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2024)
Date :
2024-02-29
Volume :
2
Publisher :
SciTePress
URI
http://hdl.handle.net/10679/9330https://www.scitepress.org/Link.aspx?doi=10.5220/0012574000003660
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