Ear recognition is a contactless and unobtrusive biometric technique with applications across various domains. However, deploying high-performing ear recognition models on resource-constrained devices is challenging, limiting their applicability and widespread adoption. This paper introduces EdgeEar, a lightweight model based on a proposed hybrid CNN-transformer architecture to solve this problem. By incorporating low-rank approximations into specific linear layers, EdgeEar reduces its parameter count by a factor of 50 compared to the current state-of-the-art, bringing it below two million while maintaining competitive accuracy. Evaluation on the Unconstrained Ear Recognition Challenge (UERC2023) benchmark shows that EdgeEar achieves the lowest EER while significantly reducing computational costs. These findings demonstrate the feasibility of efficient and accurate ear recognition, which we believe will contribute to the wider adoption of ear biometrics.

翻译：耳部识别是一种非接触式、非侵入性的生物特征识别技术，在多个领域具有应用前景。然而，在资源受限的设备上部署高性能耳部识别模型具有挑战性，这限制了其适用性和广泛采用。本文提出EdgeEar，一种基于所提出的混合CNN-Transformer架构的轻量级模型，以解决此问题。通过在特定线性层中引入低秩近似，EdgeEar的参数数量相比当前最优模型减少了50倍，降至两百万以下，同时保持了具有竞争力的准确率。在无约束耳部识别挑战赛（UERC2023）基准上的评估表明，EdgeEar实现了最低的等错误率，同时显著降低了计算成本。这些结果证明了高效准确耳部识别的可行性，我们相信这将有助于推动耳部生物特征识别技术的更广泛应用。