**StyleID：一种面向风格化无关人脸身份识别的感知感知数据集与评估指标**

摘要

创造性人脸风格化旨在将肖像以多样化的视觉风格呈现，如卡通、素描和油画，同时保持可识别的身份特征。然而，当前主要在自然照片上训练和校准的身份编码器，在风格化处理中表现出严重的脆弱性。它们常将纹理或色彩调色板的变化误判为身份漂移，或无法检测几何夸张变形。这揭示了缺乏一种风格无关的框架来评估和监控不同风格及强度下的身份一致性。为解决这一不足，我们提出了StyleID——一个面向风格化人脸身份的人类感知感知数据集与评估框架。StyleID包含两个数据集：（i）StyleBench-H，该基准通过基于扩散和流匹配的风格化方法在多种风格强度下采集人类对身份异同的验证判断；（ii）StyleBench-S，一个通过受控二选一强制选择实验获取的心理测量学识别强度曲线衍生的监督集。基于StyleBench-S，我们对现有语义编码器进行微调，使其相似性排序与人类跨风格、跨强度的感知保持一致。实验表明，经我们校准的模型与人类判断的相关性显著提高，并对域外艺术家手绘肖像表现出更强的鲁棒性。我们的全部数据集、代码与预训练模型已公开于https://kwanyun.github.io/StyleID_page/。

English

Creative face stylization aims to render portraits in diverse visual idioms such as cartoons, sketches, and paintings while retaining recognizable identity. However, current identity encoders, which are typically trained and calibrated on natural photographs, exhibit severe brittleness under stylization. They often mistake changes in texture or color palette for identity drift or fail to detect geometric exaggerations. This reveals the lack of a style-agnostic framework to evaluate and supervise identity consistency across varying styles and strengths. To address this gap, we introduce StyleID, a human perception-aware dataset and evaluation framework for facial identity under stylization. StyleID comprises two datasets: (i) StyleBench-H, a benchmark that captures human same-different verification judgments across diffusion- and flow-matching-based stylization at multiple style strengths, and (ii) StyleBench-S, a supervision set derived from psychometric recognition-strength curves obtained through controlled two-alternative forced-choice (2AFC) experiments. Leveraging StyleBench-S, we fine-tune existing semantic encoders to align their similarity orderings with human perception across styles and strengths. Experiments demonstrate that our calibrated models yield significantly higher correlation with human judgments and enhanced robustness for out-of-domain, artist drawn portraits. All of our datasets, code, and pretrained models are publicly available at https://kwanyun.github.io/StyleID_page/

StyleID：一种面向风格化无关人脸身份识别的感知感知数据集与评估指标

StyleID: A Perception-Aware Dataset and Metric for Stylization-Agnostic Facial Identity Recognition

摘要

Support