We study to what extend Chinese, Japanese and Korean faces can be classified and which facial attributes offer the most important cues. First, we propose a novel way of obtaining large numbers of facial images with nationality labels. Then we train state-of-the-art neural networks with these labeled images. We are able to achieve an accuracy of 75.03% in the classification task, with chances being 33.33% and human accuracy 38.89% . Further, we train multiple facial attribute classifiers to identify the most distinctive features for each group. We find that Chinese, Japanese and Koreans do exhibit substantial differences in certain attributes, such as bangs, smiling, and bushy eyebrows. Along the way, we uncover several gender-related cross-country patterns as well. Our work, which complements existing APIs such as Microsoft Cognitive Services and Face++, could find potential applications in tourism, e-commerce, social media marketing, criminal justice and even counter-terrorism.

该研究利用神经网络特征分类方法，对中国、日本和韩国人的面部进行分类研究，通过训练多个面部属性分类器，发现三者面部属性存在显著差异，如刘海、微笑和浓眉等，该研究有望在旅游、电子商务、社交媒体营销、刑事司法和反恐等领域得到应用。

通过精细深度学习解密中国人、日本人和韩国人是否长得相似