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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2602.08809 |
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| _version_ | 1866917260567773184 |
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| author | Haroun, Karim Zitouni, Aya Zenakhri, Aicha Guessoum, Meriem Amel Boubchir, Larbi |
| author_facet | Haroun, Karim Zitouni, Aya Zenakhri, Aicha Guessoum, Meriem Amel Boubchir, Larbi |
| contents | Recent advances in deep learning, whether on discriminative or generative tasks have been beneficial for various applications, among which security and defense. However, their increasing computational demands during training and deployment translates directly into high energy consumption. As a consequence, this induces a heavy carbon footprint which hinders their widespread use and scalability, but also a limitation when deployed on resource-constrained edge devices for real-time use. In this paper, we briefly survey efficient deep learning methods for biometric applications. Specifically, we tackle the challenges one might incur when training and deploying deep learning approaches, and provide a taxonomy of the various efficient deep learning families. Additionally, we discuss complementary metrics for evaluating the efficiency of these models such as memory, computation, latency, throughput, and advocate for universal and reproducible metrics for better comparison. Last, we give future research directions to consider. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_08809 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Efficient Deep Learning for Biometrics: Overview, Challenges and Trends in Ear of Frugal AI Haroun, Karim Zitouni, Aya Zenakhri, Aicha Guessoum, Meriem Amel Boubchir, Larbi Machine Learning Recent advances in deep learning, whether on discriminative or generative tasks have been beneficial for various applications, among which security and defense. However, their increasing computational demands during training and deployment translates directly into high energy consumption. As a consequence, this induces a heavy carbon footprint which hinders their widespread use and scalability, but also a limitation when deployed on resource-constrained edge devices for real-time use. In this paper, we briefly survey efficient deep learning methods for biometric applications. Specifically, we tackle the challenges one might incur when training and deploying deep learning approaches, and provide a taxonomy of the various efficient deep learning families. Additionally, we discuss complementary metrics for evaluating the efficiency of these models such as memory, computation, latency, throughput, and advocate for universal and reproducible metrics for better comparison. Last, we give future research directions to consider. |
| title | Efficient Deep Learning for Biometrics: Overview, Challenges and Trends in Ear of Frugal AI |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2602.08809 |