Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Do They All Look the Same? Deciphering Chinese, Japanese and Koreans by
  Fine-Grained Deep Learning   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.

17
The categories of this paper are: q-fin
29
The categories of this paper are: funct-an
11
The categories of this paper are: hep-lat
0
The categories of this paper are: hep-ph
30
The categories of this paper are: q-alg
20
The categories of this paper are: acc-phys
36
The categories of this paper are: cmp-lg
18
The categories of this paper are: econ
14
The categories of this paper are: math-ph
8
The categories of this paper are: hep-ex
16
The categories of this paper are: stat
23
The categories of this paper are: solv-int
34
The categories of this paper are: chem-ph
22
The categories of this paper are: chao-dyn
7
The categories of this paper are: hep-th
15
The categories of this paper are: nucl-ex