Efficient Deep Learning for Biometrics: Overview, Challenges and Trends in Ear of Frugal AI

• URL: http://arxiv.org/abs/2602.08809v1
• Date: Mon, 09 Feb 2026 15:48:34 GMT
• Title: Efficient Deep Learning for Biometrics: Overview, Challenges and Trends in Ear of Frugal AI
• Authors: Karim Haroun, Aya Zitouni, Aicha Zenakhri, Meriem Amel Guessoum, Larbi Boubchir,
• Abstract summary: We briefly survey efficient deep learning methods for biometric applications.<n>We tackle the challenges one might incur when training and deploying deep learning approaches.<n>We discuss complementary metrics for evaluating the efficiency of these models.
• Score: 0.9569208373364794
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Efficient Machine Unlearning via Influence Approximation [75.31015485113993]
  Influence-based unlearning has emerged as a prominent approach to estimate the impact of individual training samples on model parameters without retraining.<n>This paper establishes a theoretical link between memorizing (incremental learning) and forgetting (unlearning)<n>We introduce the Influence Approximation Unlearning algorithm for efficient machine unlearning from the incremental perspective.
  arXiv  Detail & Related papers  (2025-07-31T05:34:27Z)
• Active Learning Methods for Efficient Data Utilization and Model Performance Enhancement [5.4044723481768235]
  This paper gives a detailed overview of Active Learning (AL), which is a strategy in machine learning that helps models achieve better performance using fewer labeled examples.<n>It introduces the basic concepts of AL and discusses how it is used in various fields such as computer vision, natural language processing, transfer learning, and real-world applications.
  arXiv  Detail & Related papers  (2025-04-21T20:42:13Z)
• SHIRE: Enhancing Sample Efficiency using Human Intuition in REinforcement Learning [11.304750795377657]
  We propose SHIRE, a framework for encoding human intuition using Probabilistic Graphical Models (PGMs)<n>SHIRE achieves 25-78% sample efficiency gains across the environments we evaluate at negligible overhead cost.
  arXiv  Detail & Related papers  (2024-09-16T04:46:22Z)
• Computation-efficient Deep Learning for Computer Vision: A Survey [121.84121397440337]
  Deep learning models have reached or even exceeded human-level performance in a range of visual perception tasks. Deep learning models usually demand significant computational resources, leading to impractical power consumption, latency, or carbon emissions in real-world scenarios. New research focus is computationally efficient deep learning, which strives to achieve satisfactory performance while minimizing the computational cost during inference.
  arXiv  Detail & Related papers  (2023-08-27T03:55:28Z)
• Multi-Fidelity Active Learning with GFlowNets [65.91555804996203]
  We propose a multi-fidelity active learning algorithm with GFlowNets as a sampler, to efficiently discover diverse, high-scoring candidates. Our evaluation on molecular discovery tasks shows that multi-fidelity active learning with GFlowNets can discover high-scoring candidates at a fraction of the budget of its single-fidelity counterpart.
  arXiv  Detail & Related papers  (2023-06-20T17:43:42Z)
• Robust online active learning [0.7734726150561089]
  This work investigates the performance of online active linear regression in contaminated data streams. We propose a solution that bounds the search area of a conditional D-optimal algorithm and uses a robust estimator.
  arXiv  Detail & Related papers  (2023-02-01T13:14:26Z)
• Compute-Efficient Deep Learning: Algorithmic Trends and Opportunities [18.508401650991434]
  Economic and environmental costs of training neural networks are becoming unsustainable. Research on *algorithmically-efficient deep learning* seeks to reduce training costs through changes in the semantics of the training program. We formalize the *algorithmic speedup* problem, then use fundamental building blocks of algorithmically efficient training to develop a taxonomy.
  arXiv  Detail & Related papers  (2022-10-13T00:40:04Z)
• Design Automation for Fast, Lightweight, and Effective Deep Learning Models: A Survey [53.258091735278875]
  This survey covers studies of design automation techniques for deep learning models targeting edge computing. It offers an overview and comparison of key metrics that are used commonly to quantify the proficiency of models in terms of effectiveness, lightness, and computational costs. The survey proceeds to cover three categories of the state-of-the-art of deep model design automation techniques.
  arXiv  Detail & Related papers  (2022-08-22T12:12:43Z)
• Skill-based Meta-Reinforcement Learning [65.31995608339962]
  We devise a method that enables meta-learning on long-horizon, sparse-reward tasks. Our core idea is to leverage prior experience extracted from offline datasets during meta-learning.
  arXiv  Detail & Related papers  (2022-04-25T17:58:19Z)
• Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks [78.47459801017959]
  Sparsity can reduce the memory footprint of regular networks to fit mobile devices. We describe approaches to remove and add elements of neural networks, different training strategies to achieve model sparsity, and mechanisms to exploit sparsity in practice.
  arXiv  Detail & Related papers  (2021-01-31T22:48:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.