生成性AI的面孔:面孔的预测者
Christopher R Wolfe1, Mackenzie M Blazek1, Paige A Renschler1
1Department of Psychology, Miami University, Oxford, OH USA.
Journal of maxillofacial and oral surgery
|February 6, 2026
概括
面部外观作为核心表情尺度 (FACES) 有效区分基于积极或消极提示的AI生成的面部. 自尊评分也影响了参与者如何评价自己的面部外表.
科学领域:
- 心理学 心理学 心理学
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 面部外观作为核心表情尺度 (FACES) 是为了评估面手术患者的自我感知而开发的.
- 这项研究使用FACES来评估参与者对自己脸部的评分以及由DALL·E,一种生成性AI产生的评分.
研究的目的:
- 验证FACES尺度在使用正与负描述提示时区分人工智能生成的面部图像的能力.
- 探索自尊和面部自我感知之间的关系.
主要方法:
- 使用DALL·E生成了16个摄影现实面孔,年龄,性别,种族和描述提示 (积极/消极) 不同.
- 333名参与者使用FACES尺度对这些人工智能生成的面孔和他们自己的面孔进行了评分.
- 收集了罗森伯格自尊和国家自尊量表得分.
主要成果:
- 面孔成功地区分了所有测试图像对的正和负指令生成的面孔.
- 参与者的自尊评分与他们对自己脸部的评分相关联.
- 达尔·埃无法准确地产生现实的面异常.
结论:
- 面部尺度是区分人工智能产生的面部美学基于即时情绪的可行工具.
- 自尊是个人如何看待自己的面部外观的一个重要因素.
- 当前的生成人工智能局限性存在于描绘特定的医疗条件,如口面异常.
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