Problem
Research questions and friction points this paper is trying to address.
face recognition
monozygotic twins
facial marks
mirror asymmetry
Revisiting Face Recognition for Monozygotic Twins: The Celeb Twins Test Set
🤖 AI Summary
本文针对同卵双胞胎面部识别问题，通过构建包含80组名人双胞胎图像的Celeb Twins Test Set，并探讨了当前深度CNN匹配器未能利用皮肤标记和不对称性的问题及原因。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Celeb Twins Test Set
skin marks
mirror asymmetry
generative AI
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