Convolutional neural networks and training strategies for skin detection
This paper presents two convolutional neural networks (CNN) and their training strategies for skin detection. The first CNN, consisting of 20 convolution layers with 3 × 3 filters, is a kind of VGG network. The second is composed of 20 networkin-network (NiN) layers which can be considered a modification of Inception structure. When training these networks for human skin detection, we consider patch-based and whole-image-based training. The first method focuses on local features such as skin color and texture, and the second on the human-related shape features as well as color and texture. Experiments show that the proposed CNNs yield better performance than the conventional methods and also than the existing deep-learning based method. Also, it is found that the NiN structure generally shows higher accuracy than the VGG-based structure. The experiments also show that the whole-image-based training that learns the shape features yields better accuracy than the patch-based learning that focuses on local color and texture only.