This paper presents two novel methods for skin detection: HP-ELM and BD-SOM. Both SOM and ELM are fast for large data sets, but not yet suitable for Big Data. We show how they can be improved in order to fulfill the strict requirements for Big Data. Both new methods are described and their implementations are explained. A comparison on a large example is presented in the experiment section. We find that BD-SOM is more accurate but not as computationally efficient as HP-ELM. As a result, we show that both methods work well on a Big Data task. The given task deals with the classification of more than one billion samples (pixels) between Skin and Non Skin categories.
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