Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Exploring Transfer Learning on Face Recognition of Dark Skinned, Low
  Quality and Low Resource Face Data   There is a big difference in the tone of color of skin between dark and light
skinned people. Despite this fact, most face recognition tasks almost all
classical state-of-the-art models are trained on datasets containing an
overwhelming majority of light skinned face images. It is tedious to collect a
huge amount of data for dark skinned faces and train a model from scratch. In
this paper, we apply transfer learning on VGGFace to check how it works on
recognising dark skinned mainly Ethiopian faces. The dataset is of low quality
and low resource. Our experimental results show above 95\% accuracy which
indicates that transfer learning in such settings works.

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The categories of this paper are: hep-lat
13
The categories of this paper are: quant-ph
24
The categories of this paper are: dg-ga
8
The categories of this paper are: hep-ex
36
The categories of this paper are: cmp-lg
30
The categories of this paper are: q-alg
32
The categories of this paper are: atom-ph
21
The categories of this paper are: adap-org
18
The categories of this paper are: econ
15
The categories of this paper are: nucl-ex
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The categories of this paper are: solv-int
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The categories of this paper are: funct-an
16
The categories of this paper are: stat
6
The categories of this paper are: astro-ph
28
The categories of this paper are: alg-geom
10
The categories of this paper are: q-bio