Model Compression Techniques in Biometrics Applications: A Survey
(2401.10139)Abstract
The development of deep learning algorithms has extensively empowered humanity's task automatization capacity. However, the huge improvement in the performance of these models is highly correlated with their increasing level of complexity, limiting their usefulness in human-oriented applications, which are usually deployed in resource-constrained devices. This led to the development of compression techniques that drastically reduce the computational and memory costs of deep learning models without significant performance degradation. This paper aims to systematize the current literature on this topic by presenting a comprehensive survey of model compression techniques in biometrics applications, namely quantization, knowledge distillation and pruning. We conduct a critical analysis of the comparative value of these techniques, focusing on their advantages and disadvantages and presenting suggestions for future work directions that can potentially improve the current methods. Additionally, we discuss and analyze the link between model bias and model compression, highlighting the need to direct compression research toward model fairness in future works.
Overview
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Deep learning models in biometrics face issues of high memory and computational demands, especially when deployed on edge devices.
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Model compression techniques such as quantization, knowledge distillation, and pruning are researched to make models more resource-efficient.
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While these techniques can maintain model accuracy, they may inadvertently amplify biases against certain demographic groups in biometrics.
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The paper advocates for fairness-aware compression techniques and the use of balanced datasets to mitigate bias in compressed models.
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It marks the first comprehensive review of model compression in biometrics, emphasizing the balance between performance improvement and fairness.
Overview of Model Compression Techniques in Biometrics
Introduction to Model Compression in Biometrics
Modern deep learning models have significantly advanced automation capabilities, but their increasing complexity has raised several issues, including high memory demands and computational costs. In biometrics applications such as facial or iris recognition, these limitations are more pronounced because these systems are often deployed on resource-constrained edge devices. This has driven research towards model compression techniques, which aim to reduce the resource footprint of deep learning models without substantially sacrificing performance. Model compression is of particular interest in biometric applications due to their widespread use in resource-constrained environments.
Advantages and Challenges
Within the realm of model compression, three primary techniques are often utilized: quantization, knowledge distillation (KD), and pruning. Each technique has its advantages and peculiarities:
- Quantization involves decreasing the precision of the numerical variables in the model, allowing for faster and more memory-efficient operations.
- Knowledge Distillation is the process where a smaller "student" model learns from a larger "teacher" model to perform similarly without the bulk of the original model.
- Pruning refers to the technique of eliminating unnecessary weights or neurons in a neural network, resulting in a sparser but still effective model.
However, while these techniques can yield more compact models with little loss in accuracy, their individual application and results are nuanced. They each carry some challenges, especially when the goal is to maintain fairness and prevent increased bias against certain sub-groups in the processed data.
Fairer Compression Research Directions
The paper highlights that these compression methods sometimes inadvertently magnify biases present in the original models. For instance, after model compression, biometric systems can yield even more biased results against specific demographic groups. This situation underscores the need for research focused on understanding and mitigating these unintended consequences.
To tackle this, the paper suggests several approaches, including the development of fairness-aware compression techniques, the use of balanced datasets, and enhancing algorithms with the capability to recognize and minimize bias during the compression process.
Summary and Future Work
The survey conducted in the paper stands as the first comprehensive review of compression techniques in biometric applications, analyzing not only the quantitative effects of compression on model performance but also highlighting the qualitative impacts on model bias and fairness. The paper underscores the importance of future research that aims to improve current methods, suggesting a pathway towards compression techniques that enhance performance while also considering the crucial aspect of fairness.
By calling for more nuanced studies and technically mindful developments in the field, this paper contributes to paving the way for advanced and equitable biometric systems that are both resource-efficient and socially responsible.
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