人工智能应用于目击者识别的应用
Heather Kleider-Offutt1, Beth Stevens2, Laura Mickes3
1Department of Psychology, Georgia State University, Atlanta, GA, 30030, USA. hoffutt@gsu.edu.
Cognitive research: principles and implications
|April 3, 2024
概括
面部识别系统 (FRS) 在识别犯罪视频中的犯罪者方面优于人类目击者. 这种人工智能工具提供可靠的嫌疑人匹配,对于执法应用至关重要.
科学领域:
- 认知心理学 认知心理学
- 计算机科学 计算机科学
- 法医科学 法医科学 法医科学
背景情况:
- 面部识别系统 (FRS) 在执法部门越来越多地用于嫌疑人的识别.
- 没有先前的研究直接比较FRS准确度与目击者准确度,使用视频证据.
- 了解FRS相对于人类记忆的能力对于优化调查程序至关重要.
研究的目的:
- 为了比较开源FRS (FaceNet) 与人类目击者在视频识别犯罪者的表现的准确性.
- 调查视频清晰度和犯罪者的种族对FRS和目击者的识别准确性的影响.
主要方法:
- 参与者和FaceNet FRS在观看不同清晰度和罪犯种族的犯罪视频后,从照片排队中识别了罪犯.
- FRS生成了视频探测器和阵容成员之间的相似度等级.
- 采用接收器运行特征 (ROC) 分析和信心准确性特征 (CAC) 分析来衡量可区分性和可靠性.
主要成果:
- 在所有条件下,无论视频清晰度或犯罪者的种族,FRS的表现都明显超过了目击者的表现.
- 较低的视频清晰度对人类目击者的准确性产生了负面影响.
- FRS表现出高可靠性,在被赋予高相似度分数时准确识别罪犯.
结论:
- 像FaceNet这样的开源FRS在视频中识别犯罪者时,与人类目击者相比,显示出更高的准确性和可靠性.
- 在嫌疑人搜索和队列构建中,FRS可以作为目击者记忆的宝贵补充.
- 进一步研究FRS在法医应用中的优缺点是必不可少的.
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