Eye movement biometrics is a secure and innovative identification method. Deep learning methods have shown good performance, but their network architecture relies on manual design and combined priori knowledge. To address these issues, we introduce automated network search (NAS) algorithms to the field of eye movement recognition and present Relax DARTS, which is an improvement of the Differentiable Architecture Search (DARTS) to realize more efficient network search and training. The key idea is to circumvent the issue of weight sharing by independently training the architecture parameters $\alpha$ to achieve a more precise target architecture. Moreover, the introduction of module input weights $\beta$ allows cells the flexibility to select inputs, to alleviate the overfitting phenomenon and improve the model performance. Results on four public databases demonstrate that the Relax DARTS achieves state-of-the-art recognition performance. Notably, Relax DARTS exhibits adaptability to other multi-feature temporal classification tasks.

翻译：眼动生物识别是一种安全且创新的身份认证方法。深度学习方法已展现出良好性能，但其网络架构依赖于人工设计与先验知识组合。为解决这些问题，我们将自动化网络搜索（NAS）算法引入眼动识别领域，并提出松弛DARTS——这是对可微分架构搜索（DARTS）的改进，旨在实现更高效的网络搜索与训练。其核心思想是通过独立训练架构参数$\alpha$以规避权重共享问题，从而获得更精确的目标架构。此外，引入模块输入权重$\beta$使单元能灵活选择输入，以缓解过拟合现象并提升模型性能。在四个公开数据库上的实验结果表明，松弛DARTS实现了最先进的识别性能。值得注意的是，松弛DARTS对其他多特征时序分类任务也展现出良好的适应性。