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

Do They All Look the Same? Deciphering Chinese, Japanese and Koreans by Fine-Grained Deep Learning