用大规模眼周数据训练深度模型，显著提升识别精度。
Leveraging Large-Scale Face Datasets for Deep Periocular Recognition via Ocular Cropping
- 从VGGFace2提取百万级眼周图像，训练深层CNN模型。
- 在UFPR数据集上达1-2%错误率，创该数据集最低纪录。
- 适合关注生物特征识别、小样本场景下性能优化的研究者。
本文聚焦眼周生物特征识别，即眼睛周围区域的识别，该区域具有高区分度且采集条件宽松。我们评估了三种不同深度与复杂度的卷积神经网络架构在眼周识别任务中的表现。模型基于从大规模VGGFace2数据库中提取的1,907,572张眼周图像进行训练，这与以往依赖仅数千张图像的小规模眼周数据集形成鲜明对比。实验使用VGGFace2-Pose（包含野外人脸图像）和UFPR-Periocular（由用户在手机屏幕上引导拍摄的自拍照）两个数据集。由于VGGFace2数据采集环境不受控，其眼周图像的等错误率（EER）为9%-15%，明显高于全脸图像的3%-6%。而UFPR-Periocular因图像质量更高、采集流程更一致，实现1%-2%的优异性能，据我们所知是目前该数据集报告的最低EER。
原文摘要 · Abstract (English)
We focus on ocular biometrics, specifically the periocular region (the area around the eye), which offers high discrimination and minimal acquisition constraints. We evaluate three Convolutional Neural Network architectures of varying depth and complexity to assess their effectiveness for periocular recognition. The networks are trained on 1,907,572 ocular crops extracted from the large-scale VGGFace2 database. This significantly contrasts with existing works, which typically rely on small-scale periocular datasets for training having only a few thousand images. Experiments are conducted with ocular images from VGGFace2-Pose, a subset of VGGFace2 containing in-the-wild face images, and the UFPR-Periocular database, which consists of selfies captured via mobile devices with user guidance on the screen. Due to the uncontrolled conditions of VGGFace2, the Equal Error Rates (EERs) obtained with ocular crops range from 9-15%, noticeably higher than the 3-6% EERs achieved using full-face images. In contrast, UFPR-Periocular yields significantly better performance (EERs of 1-2%), thanks to higher image quality and more consistent acquisition protocols. To the best of our knowledge, these are the lowest reported EERs on the UFPR dataset to date.
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