发表机构
University of Notre Dame（圣母大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究构建了含80组名人双胞胎的CTTS测试集，发现当前深度CNN匹配器未利用皮肤特征与镜像不对称，还探讨了用Grok等生成式AI工具扩充双胞胎训练集的可行性。
AI 中文摘要
过往关于同卵（“完全相同”）双胞胎人脸识别的文献指出，面部特征和镜像不对称是提升双胞胎识别准确率的可能方向。Celeb Twins测试集（CTTS）包含通过网络爬取的80组名人双胞胎图像对，是唯一拥有带可区分皮肤特征及可能存在镜像不对称的双胞胎元数据的双胞胎测试集。CTTS的组织方式与LFW、CALFW、CPLFW、CFP-FP和AgeDB-30等人脸验证测试集类似。当前的深度卷积神经网络（CNN）匹配器在对CTTS的同一人/不同人图像对进行分类时，准确率可超过76%。研究表明，当前匹配器并未利用皮肤特征或不对称性，并讨论了其中的原因。最后，研究探讨了使用Grok、ChatGPT和Gemini等生成式AI工具创建想象中的同卵双胞胎图像，以增加双胞胎在人脸识别训练集中的代表性的可行性。
英文摘要
Past literature on face recognition for monozygotic (("identical") twins points to facial marks and mirror asymmetry as possible directions for improved accuracy of twins recognition. The Celeb Twins Test Set (CTTS) contains web-scraped image pairs for 80 sets of celebrity twins. It is the only twins test set with meta-data for twins with distinguishing skin marks and possible mirror asymmetry. CTTS is organized in the manner of face verification test sets such as LFW, CALFW, CPLFW, CFP-FP, and AgeDB-30. Current deep CNN matchers can achieve over 76% accuracy in classifying CTTS same-person / different-person image pairs. We show that current matchers do not make use of skin marks, or asymmetry, and discuss reasons for this. Finally, we discuss the feasibility of using generative AI tools such as Grok, ChatGPT and Gemini to create images of imagined monozygotic twins as a means to increase representation of twins in face recognition training sets.
