Author
Dube, D. C., Eroğlu Erdem, Ciğdem, Korcak, Ö.
Publication Date
2024-02-29
Publication Place
-
SciTePress
Subject
Face recognition, Deep learning, Curriculum learning, Federated learning
Type
Document
Language
English
Digital
Yes
Manuscript
No
Library
Özyeğin University
Library Asset ID
2184-4321
Record ID
20a7496d-20be-46b5-ac08-de48267eb3ca
Library Location
Electrical & Electronics Engineering
Date
2024-02-29
Sample Text
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.
DOI
10.5220/0012574000003660
Cilt
2
