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UPSI Digital Repository (UDRep)
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| Abstract : Universiti Pendidikan Sultan Idris |
| The aim of this study is to design and develop a systematic dataset for multiple skin tones and to
analyse the reasons behind misclassifications of skin and non-skin, using different deep learning
models, colour spaces, and different optimisation parameters. Related academic literature have
cited three problems, namely data-related issues (e.g. skin-like), data volume (e.g. large volume
requires high computer source), and technical issues (e.g. optimising parameters). Two
articles on Deep Learning (DL) for skin detection failed to address the issues extensively.
DL foundation is a training dataset and the quality of training depends on the quality of
the data-input. To address the issues, a systematic dataset consisting of 17 million patches
was created for multiple skin tones with (skin-like) images. The dataset was then converted into
different colour spaces with multiple labels that characterise different scenarios, running
different DL. Experimentally utilised YCbCr and CNN present high performance of binary
and multi-class classifications. Binary classification of skin and skin-like resulted in 98% and
multi-class classification of four classes 84% and 69% for five classes respectively. Furthermore,
a binary classification between skin tone and skin-like (e.g. black skin tone and black
skin-like) resulted in 97%, 81%, 60%, and 51% for black, brown, medium, and fair
consequently. From empirical experiment, darker skin tone is a better classification accuracy
followed by optimising parameters (Hidden-Layers, Neurons, Activations-Functions, Optimiser,
Initialiser, Data-Input, and Data-Size). A hybrid CNN-RNN benchmark improves the accuracy by
99% compared with 98%, and 97% for SAE compared to 91% as reported. By studying different skin
scenarios, one can analyse the reasons behind overlapping between skin and skin-tones.
This is a promising study for further research by developing and applying a generalised version
of skin detector with different applications.
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