Name
Abstract | ||
|---|---|---|
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 x 3 filters, is a kind of VGG network. The second is composed of 20 network in-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. |
Year | Venue | Keywords |
|---|---|---|
2017 | 2017 24TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) | skin detection, deep learning, convolutional network, CNN |
Field | DocType | ISSN |
Computer vision,Pattern recognition,Local color,Convolutional neural network,Computer science,Convolution,Artificial intelligence | Conference | 1522-4880 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yoonsik Kim | 1 | 11 | 3.87 |
| Insung Hwang | 2 | 16 | 3.25 |
| Nam Ik Cho | 3 | 712 | 106.98 |