Eye movement biometrics has received increasing attention thanks to its highly secure identification. Although deep learning (DL) models have shown success in eye movement recognition, their architectures largely rely on human prior knowledge. Differentiable Neural Architecture Search (DARTS) automates the manual process of architecture design with high search efficiency. However, DARTS typically stacks multiple cells to form a convolutional network, which limits the diversity of architecture. Furthermore, DARTS generally searches for architectures using shallower networks than those used in the evaluation, creating a significant disparity in architecture depth between the search and evaluation phases. To address this issue, we propose EM-DARTS, a hierarchical differentiable architecture search algorithm to automatically design the DL architecture for eye movement recognition. First, we define a supernet and propose a global and local alternate Neural Architecture Search method to search the optimal architecture alternately with a differentiable neural architecture search. The local search strategy aims to find an optimal architecture for different cells while the global search strategy is responsible for optimizing the architecture of the target network. To minimize redundancy, transfer entropy is proposed to compute the information amount of each layer, thereby further simplifying the network search process. Experimental results on three public datasets demonstrate that the proposed EM-DARTS is capable of producing an optimal architecture that leads to state-of-the-art recognition performance, {Specifically, the recognition models developed using EM-DARTS achieved the lowest EERs of 0.0453 on the GazeBase dataset, 0.0377 on the JuDo1000 dataset, and 0.1385 on the EMglasses dataset.

翻译：眼动生物识别技术因其高安全性而受到越来越多的关注。尽管深度学习模型在眼动识别方面已取得成功，但其架构在很大程度上依赖于人类的先验知识。可微分神经架构搜索通过高搜索效率实现了架构设计的手动过程自动化。然而，DARTS通常堆叠多个单元以形成卷积网络，这限制了架构的多样性。此外，DARTS通常使用比评估阶段更浅的网络来搜索架构，导致搜索与评估阶段在架构深度上存在显著差异。为解决此问题，我们提出了EM-DARTS，一种用于自动设计眼动识别深度学习架构的分层可微分架构搜索算法。首先，我们定义了一个超网络，并提出了一种全局与局部交替的神经架构搜索方法，通过可微分神经架构搜索交替搜索最优架构。局部搜索策略旨在为不同单元寻找最优架构，而全局搜索策略则负责优化目标网络的架构。为最小化冗余，我们提出了转移熵来计算每层的信息量，从而进一步简化网络搜索过程。在三个公开数据集上的实验结果表明，所提出的EM-DARTS能够生成实现最先进识别性能的最优架构。具体而言，使用EM-DARTS开发的识别模型在GazeBase数据集上达到了0.0453的最低等错误率，在JuDo1000数据集上为0.0377，在EMglasses数据集上为0.1385。