轻量级耳部识别模型，让边缘设备也能高效准确识耳。
EdgeEar: Efficient and Accurate Ear Recognition for Edge Devices
- 用混合卷积与注意力结构，结合低秩近似压缩模型。
- 参数量减至两百万以下，比顶尖模型少50倍。
- 在真实场景数据集上实现最低误识率，适合边缘部署。
耳部识别是一种无接触、无侵入的生物特征技术，应用广泛。但在资源受限的边缘设备上部署高性能模型仍具挑战性。本文提出EdgeEar，一种基于新型混合卷积-变压器架构的轻量级模型。通过在特定线性层中引入低秩近似，其参数量相比当前最先进模型减少50倍，降至两百万以下，同时保持竞争力的识别精度。在无约束耳部识别挑战赛（UERC2023）基准测试中，EdgeEar实现了最低等错误率（EER），且计算开销显著降低。结果表明，该方法在保证高精度的同时具备极强的效率，有望推动耳部生物特征技术在边缘设备上的广泛应用。
原文摘要 · Abstract (English)
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.
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