StyleID：一個感知感知資料集與用於風格化無關人臉身份識別的評估指標

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

April 23, 2026

作者: Kwan Yun, Changmin Lee, Ayeong Jeong, Youngseo Kim, Seungmi Lee, Junyong Noh

cs.AI

摘要

創意臉部風格化旨在以多樣化的視覺語彙（如卡通、素描、繪畫）渲染肖像，同時保持可辨識的身份特徵。然而，當前主要在自然照片上訓練與校準的身份編碼器，在風格化處理下表現出嚴重的脆弱性。它們常將紋理或色調的變化誤判為身份漂移，或無法檢測幾何誇張效果。這揭示了缺乏一種與風格無關的框架來評估和監督不同風格及強度下的身份一致性。為解決此問題，我們提出 StyleID——一個針對風格化臉部身份的人類感知感知資料集與評估框架。StyleID 包含兩個資料集：（i）StyleBench-H，該基準透過基於擴散模型與流匹配的風格化技術，在多種風格強度下捕捉人類對身份異同的驗證判斷；（ii）StyleBench-S，一個透過受控的雙選強制選擇（2AFC）實驗獲得的心理測量識別強度曲線所衍生的監督集。利用 StyleBench-S，我們對現有語義編碼器進行微調，使其相似度排序與人類跨風格和強度的感知保持一致。實驗表明，經校準的模型與人類判斷具有顯著更高的相關性，並對領域外的藝術家手繪肖像展現出更強的魯棒性。所有資料集、程式碼與預訓練模型均公開於 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/