双胞胎识别超过观点变化:一个深层次的卷积神经网络超越了人类.
Connor J Parde1, Virginia E Strehle1, Vivekjyoti Banerjee2
1School of Behavioral and Brain Sciences, The University of Texas at Dallas, USA.
ACM transactions on applied perception
|August 12, 2024
概括
深层卷积神经网络 (DCNNs) 与人脸识别的准确性相匹配,即使是非常相似的面孔,如同卵双胞胎. 在不同观点上,DCNN在区分身份方面表现与人类相当或比人类更好.
科学领域:
- 计算机视觉 计算机视觉
- 认知科学 认知科学
- 生物识别信息 生物识别信息
背景情况:
- 深层卷积神经网络 (DCNNs) 在面部识别任务中表现出人类水平的准确性.
- 对于DCNN在高度相似的面孔之间进行区分的能力,例如同卵双胞胎,仍然不太了解.
- 人类的面部感知能力已经很成熟,但可能会受到微妙的变化和高度相似的个体的挑战.
研究的目的:
- 为了比较人类和DCNN在涉及同卵双胞胎的具有挑战性的面部身份匹配任务中的表现.
- 评估观点差异如何影响人类和DCNN在歧视面部身份的准确性.
- 为了研究人类和DCNN的相似性判断之间的相关性,对各种面孔对型.
主要方法:
- 对87名人类参与者和DCNN进行了面部身份匹配任务.
- 图像对包括相同身份,一般伪造者和相同双胞胎伪造者类别.
- 在三种视角差异条件下进行了比较:正面到正面,正面到45°,正面到90°.
主要成果:
- 人类准确性对于一般的假冒者来说比双胞胎假冒者更高,随着观点差异的增加而下降.
- 在大多数条件下,DCNN反映了人类的准确性模式,在人类水平或以上的性能.
- 人类和DCNN相似度得分显示,在九种图像对类型中的六种中,存在显著的相关性.
结论:
- 在区分高度相似的面孔方面,DCNN表现出强的表现,与人类能力相比或优于人类能力.
- 这些发现表明,DCNN利用与人类感知策略一致的面部歧视特征.
- 这项研究促进了对DCNN在具有挑战性的生物识别应用中的理解,并突出了它们在法医和安全背景中的潜力.
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