用方向特征增强CNN，提升眼周识别准确率
Explicit Over Implicit: Enhancing CNNs Via Complex Structure Tensor Representations for Periocular Recognition
- 用复数结构张量提取方向信息作为输入
- 在两个数据集上降低5%-26%错误率
- 适合资源受限设备的高效生物识别
本研究发现CNN难以有效提取方向特征。通过将包含置信度的方向特征（复数结构张量）输入CNN，相比仅使用灰度图，识别准确率显著提升。实验表明，由小型复数卷积网络生成的输入配合压缩版CNN，性能优于完整主流CNN架构。该方法借鉴哺乳动物视觉机制，在闭世界与开世界场景下均有效缓解了CNN的表征局限，同时提升了可解释性与对轻量级设备的适用性。在Cross-Eyed和PolyU两个公开眼周图像数据集上，等错误率（EER）降低5%-26%，验证了显式方向先验的有效性。
原文摘要 · Abstract (English)
Our study provides evidence that CNNs struggle to extract orientation features effectively. We show that using the Complex Structure Tensor, which contains compact orientation features with certainties, as input to CNNs consistently improves identification accuracy compared to grayscale inputs alone. Experiments also demonstrated that our inputs, provided by mini-complex convnets, combined with reduced CNN sizes, outperformed full-fledged, prevailing CNN architectures. This suggests that the upfront use of orientation features in CNNs, a strategy seen in mammalian vision, not only mitigates their limitations but also enhances their explainability and relevance to thin-clients. Experiments were conducted on publicly available datasets comprising periocular images (Cross-Eyed and PolyU) for biometric identification and verification in both Close-World and Open-World Scenarios using six CNN architectures. Our experiments on the Cross-Eyed and PolyU datasets yield a 5-26% reduction in EER, providing strong empirical evidence that explicit orientation priors mitigate CNN representational limits in Open-World and Close-World scenarios.
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