Abstract
Lightweight model development has emerged as an important study subject in computer vision in response to the need for resource-efficient solutions. These models attempt to strike a balance between model size, computing requirements, and accuracy. They give benefits such as efficient resource use, faster inference times, and improved accessibility. For 3D facial reconstruction models, lightweight architectures present an opportunity for implementation in less demanding hardware, since these algorithms usually rely on powerful processors such as NVIDIA graphic cards. The following research paper provides a benchmark comparison between diverse state-of-the-art lightweight models in a facial reconstruction model, with the aim to reduce its computational complexity so that it can be tested on a mobile device.
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Acknowledgment
The authors would like to acknowledge the financial support of Tecnologico de Monterrey through the program “Challenge-Based Research Funding Program 2022”. Project ID # E120 - EIC-GI06 - B-T3 - D.
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Hernández-Manrique, V., González-Mendoza, M., Vilchis, C., Méndez-Ruiz, M., Pérez-Guerrero, C. (2024). Benchmark Analysis for Backbone Optimization in a Facial Reconstruction Model. In: Calvo, H., Martínez-Villaseñor, L., Ponce, H. (eds) Advances in Computational Intelligence. MICAI 2023. Lecture Notes in Computer Science(), vol 14391. Springer, Cham. https://doi.org/10.1007/978-3-031-47765-2_11
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