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SqueezerFaceNet: Reducing a Small Face Recognition CNN Even More Via Filter Pruning
Halmstad University, School of Information Technology, Center for Applied Intelligent Systems Research (CAISR).ORCID iD: 0000-0002-1400-346X
Halmstad University, School of Information Technology, Center for Applied Intelligent Systems Research (CAISR).ORCID iD: 0000-0002-9696-7843
Computer Graphics and Vision and AI Group, University of Balearic Islands, Palma, Spain.
Halmstad University, School of Information Technology, Center for Applied Intelligent Systems Research (CAISR).ORCID iD: 0000-0002-4929-1262
2024 (English)In: Progress in Artificial Intelligence and Pattern Recognition. IWAIPR 2023. / [ed] Hernández Heredia, Y.; Milián Núñez, V.; Ruiz Shulcloper, J., Cham: Springer, 2024, Vol. 14335, p. 349-361Conference paper, Published paper (Refereed)
Abstract [en]

The widespread use of mobile devices for various digital services has created a need for reliable and real-time person authentication. In this context, facial recognition technologies have emerged as a dependable method for verifying users due to the prevalence of cameras in mobile devices and their integration into everyday applications. The rapid advancement of deep Convolutional Neural Networks (CNNs) has led to numerous face verification architectures. However, these models are often large and impractical for mobile applications, reaching sizes of hundreds of megabytes with millions of parameters. We address this issue by developing SqueezerFaceNet, a light face recognition network which less than 1M parameters. This is achieved by applying a network pruning method based on Taylor scores, where filters with small importance scores are removed iteratively. Starting from an already small network (of 1.24M) based on SqueezeNet, we show that it can be further reduced (up to 40%) without an appreciable loss in performance. To the best of our knowledge, we are the first to evaluate network pruning methods for the task of face recognition. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Place, publisher, year, edition, pages
Cham: Springer, 2024. Vol. 14335, p. 349-361
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 14335
Keywords [en]
Face recognition, Mobile Biometrics, CNN pruning, Taylor scores
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:hh:diva-51299DOI: 10.1007/978-3-031-49552-6_30Scopus ID: 2-s2.0-85180788350ISBN: 978-3-031-49551-9 (print)ISBN: 978-3-031-49552-6 (electronic)OAI: oai:DiVA.org:hh-51299DiVA, id: diva2:1783322
Conference
VIII International Workshop on Artificial Intelligence and Pattern Recognition, IWAIPR, Varadero, Cuba, September 27-29, 2023
Part of project
Facial Analysis in the Era of Mobile Devices and Face Masks, Swedish Research Council
Funder
Swedish Research CouncilVinnova
Note

Funding: F. A.-F., K. H.-D., and J. B. thank the Swedish Research Council (VR) and the Swedish Innovation Agency (VINNOVA) for funding their research. Author J. M. B. thanks the project EX-PLAINING - "Project EXPLainable Artificial INtelligence systems for health and well-beING", under Spanish national projects funding (PID2019-104829RA-I00/AEI/10.13039/501100011033).

Available from: 2023-07-20 Created: 2023-07-20 Last updated: 2024-06-17Bibliographically approved

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Alonso-Fernandez, FernandoHernandez-Diaz, KevinBigun, Josef

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