The appraisal of trustworthiness from facial appearance of a stranger is critical for successful social interaction. Although self-resemblance is considered a significant factor affecting the perception of trustworthiness, research is yet to be conducted on whether this theory is applicable to natural unfamiliar faces in real life. We examined this aspect by using a state-of-the-art deep convolutional neural network for face recognition to measure the facial similarity of a large sample of people with the evaluators. We found that the more they resembled the rater, the more trustworthy they were evaluated if they were of the same sex as the rater. Contrarily, when the stranger was of the opposite sex, self-resemblance did not affect trustworthiness ratings. These results demonstrate that self-resemblance is an important factor affecting our social judgments of especially same-sex people in real life.

从陌生人的面部外观评估可信度对于成功的社交互动至关重要。尽管自相似性被认为是影响可信度感知的重要因素，但该理论是否适用于现实生活中自然不熟悉的面孔仍有待研究。我们通过使用最先进的深度卷积神经网络进行人脸识别来测量大样本与评估者的面部相似性，从而检验了这一方面。我们发现，他们越像评估者，如果他们与评估者的性别相同，他们被评估的可信度就越高。相反，当陌生人是异性时，自我相似性不会影响可信度评级。