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

Westudy to what extend Chinese, Japaneseand 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 theclassification task, with chancesbeing 33.33% and human accuracy 38.89% . Further, we train multiple facial attribute classifiers to identify the most distinctive features for each group. Wefind that Chinese, Japaneseand Koreansdo exhibit substantial differences in certain attributes, such as bangs, smiling, and bushy eyebrows. Along theway, weuncover several gender-related cross-country patterns as well. Our work, which complements existing APIs such as Microsoft CognitiveServicesand Face++, could find potential applications in tourism, e-commerce, social media marketing, criminal justice and even counter-terrorism.