Towards an Efficient Facial Image Compression with Neural Networks

2022
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Authors: Maria Ausilia Napoli Spatafora; Alessandro Ortis; Sebastiano Battiato
Published in: Image Analysis and Processing – ICIAP 2022
Publisher: Springer International Publishing

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Abstract

The chapter delves into the critical role of facial image compression in various applications, emphasizing the need for efficient algorithms that preserve face recognition capabilities. It introduces a novel convolutional autoencoder architecture designed specifically for facial images, incorporating a custom loss function that integrates image compression with face recognition tasks. The proposed method is validated through extensive experiments, demonstrating superior performance compared to traditional compression standards like JPG. The results highlight the potential of neural networks in addressing the unique challenges of facial image compression, offering insights for future research in this domain.

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Title: Towards an Efficient Facial Image Compression with Neural Networks
Authors: Maria Ausilia Napoli Spatafora
Alessandro Ortis
Sebastiano Battiato
Publisher: Springer International Publishing
Book: Image Analysis and Processing – ICIAP 2022
Print ISBN: 978-3-031-06426-5

Electronic ISBN: 978-3-031-06427-2

Copyright Year: 2022
DOI: https://doi.org/10.1007/978-3-031-06427-2_43

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