为历史报纸人脸检测构建新数据集，提升识别准确率。
Archival Faces: Detection of Faces in Digitized Historical Documents
- 构建2.2万张历史报纸图像数据集，含11000个标注框和关键点。
- 重新训练模型后，检测准确率（mAP）提升至接近野外人脸检测标准。
- 适合从事历史文献数字化、图像识别的科研与工程人员使用。
在数字化历史档案时，需识别名人与普通人面部，并与周边文本关联以实现搜索。现有针对扫描历史文档的人脸检测工具表现不佳，当前检测器在50:90%交并比下的平均精度（mAP）仅约24%。本文通过引入一个新的人工标注领域专用数据集，风格类似Wider Face，包含2.2千张19至20世纪数字报刊图像，共11,000个新边界框标注及对应面部关键点。该数据集使现有检测器可重新训练，显著提升性能，使其更接近真实场景下人脸检测的标准。实验对比了多种微调检测器与公开预训练模型，并进行了多尺寸检测器的消融研究，全面评估了检测与关键点预测性能。
原文摘要 · Abstract (English)
When digitizing historical archives, it is necessary to search for the faces of celebrities and ordinary people, especially in newspapers, link them to the surrounding text, and make them searchable. Existing face detectors on datasets of scanned historical documents fail remarkably -- current detection tools only achieve around 24% mAP at 50:90% IoU. This work compensates for this failure by introducing a new manually annotated domain-specific dataset in the style of the popular Wider Face dataset, containing 2.2k new images from digitized historical newspapers from the 19th to 20th century, with 11k new bounding-box annotations and associated facial landmarks. This dataset allows existing detectors to be retrained to bring their results closer to the standard in the field of face detection in the wild. We report several experimental results comparing different families of fine-tuned detectors against publicly available pre-trained face detectors and ablation studies of multiple detector sizes with comprehensive detection and landmark prediction performance results.
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