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

TL;DR

This paper surveys efficient deep learning methods for biometric applications, focusing on challenges, taxonomy, evaluation metrics, and future directions to enable deployment on resource-constrained devices.

Contribution

It provides a comprehensive taxonomy of efficient deep learning techniques for biometrics and discusses evaluation metrics and future research directions.

Findings

Taxonomy of efficient deep learning methods for biometrics

Discussion on metrics like memory, computation, latency, throughput

Identification of challenges in training and deploying deep models

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.