Skin detection in digital images with artificial neural networks

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The increasing capacity of data processing in personal computers and devices could develop filters and automatic classifiers working in real time and applied in several areas. Considering Digital Image Processing and Artificial Neural Networks, these filters emulate the human perception searching for patterns in order to identify specific features. Filters which the main goal is to restrict the access to inappropriate content starts identifying skin tones - the main evidence of human presence in a picture. Although being complex and robust, if the classifier is not able to identify distinct skin tones under random capture conditions, the accuracy is minimal. Facing several ways on describing skin tones over different color spaces, this work uses the RGB, YCbCr and HSV color spaces which are widely applied in recording devices (photographic and digital cameras for example). Based on the examples shown during the training phase, the ANNs must be able to classify skin tones into two distinct groups: skin and non skin.



artificial neural network, digital image processing, pattern recognition

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2018 Argentine Conference On Automatic Control (aadeca). New York: Ieee, 6 p., 2018.