Mallick Snipta, Jeckeln Géraldine, Parde Connor J, Castillo Carlos D, O'Toole Alice J
School of Behavioral and Brain Sciences, The University of Texas at Dallas, Richardson, TX.
Whiting School of Engineering, Johns Hopkins University, College Park, MD.
ACM Trans Appl Percept. 2024 Jan;21(1). doi: 10.1145/3618113. Epub 2023 Dec 9.
Facial morphs created between two identities resemble both of the faces used to create the morph. Consequently, humans and machines are prone to mistake morphs made from two identities for either of the faces used to create the morph. This vulnerability has been exploited in "morph attacks" in security scenarios. Here, we asked whether the "other-race effect" (ORE)-the human advantage for identifying own- vs. other-race faces-exacerbates morph attack susceptibility for humans. We also asked whether face-identification performance in a deep convolutional neural network (DCNN) is affected by the race of morphed faces. Caucasian (CA) and East-Asian (EA) participants performed a face-identity matching task on pairs of CA and EA face images in two conditions. In the morph condition, different-identity pairs consisted of an image of identity "A" and a 50/50 morph between images of identity "A" and "B". In the baseline condition, morphs of different identities never appeared. As expected, morphs were identified mistakenly more often than original face images. Of primary interest, morph identification was substantially worse for cross-race faces than for own-race faces. Similar to humans, the DCNN performed more accurately for original face images than for morphed image pairs. Notably, the deep network proved substantially more accurate than humans in both cases. The results point to the possibility that DCNNs might be useful for improving face identification accuracy when morphed faces are presented. They also indicate the significance of the race of a face in morph attack susceptibility in applied settings.
在两个身份之间创建的面部变形图像与用于创建变形的两张脸都相似。因此，人类和机器都容易将由两个身份创建的变形误认为是用于创建变形的任何一张脸。这种漏洞在安全场景中的“变形攻击”中被利用。在这里，我们询问“异族效应”（ORE）——人类在识别自己种族与其他种族面孔方面的优势——是否会加剧人类对变形攻击的易感性。我们还询问了深度卷积神经网络（DCNN）中的面部识别性能是否会受到变形面孔种族的影响。高加索人（CA）和东亚人（EA）参与者在两种条件下对CA和EA面部图像对执行面部身份匹配任务。在变形条件下，不同身份的图像对由身份“A”的图像和身份“A”与“B”的图像之间的50/50变形组成。在基线条件下，不同身份的变形图像从未出现。正如预期的那样，变形图像被误认的频率比原始面部图像更高。最主要的是，跨种族面孔的变形识别比同种族面孔差得多。与人类相似，DCNN对原始面部图像的识别比对变形图像对更准确。值得注意的是，在这两种情况下，深度网络都比人类准确得多。结果表明，当呈现变形面孔时，DCNN可能有助于提高面部识别准确率。它们还表明了面部种族在应用环境中对变形攻击易感性的重要性。
