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A twin support vector machine based approach to classifying human skin
- Source :
- 2018 4th International Conference on Computing Communication and Automation (ICCCA).
- Publication Year :
- 2018
- Publisher :
- IEEE, 2018.
-
Abstract
- The classification of color image that contain skin pixel is a challenging process. It is challenging due to certain factor such as brightness, background similar to human skin represent the obstacles thereof. The basic approach of skin detection is color based classification. The human skin image is the composition of RGB color. The data values of skin is calculated by randomly sampling RGB color values of different face images which have different age, race, genders sets. However, its performance has not really been high because of the high overlapped degree between “skin” and “non-skin” pixels. This paper describes the linear norm fuzzy based twin support vector machine (LN-FTSVM)) approach for discriminate skin and non-skin data values of skin dataset and to enhance the skin recognition performance. The concept of fuzzy is resolved the unclassified and overlapped data region problems. If no decision function is positive for a data set, this data set is classified into a class with the large membership value. By computational experiments shows that Experiments result shows that the accuracy is improved by linear norm fuzzy based TSVM (LN-FTSVM)) over the conventional methods.
- Subjects :
- integumentary system
Pixel
business.industry
Color image
Computer science
010401 analytical chemistry
Pattern recognition
02 engineering and technology
01 natural sciences
Fuzzy logic
0104 chemical sciences
Support vector machine
Data set
Statistical classification
Norm (mathematics)
0202 electrical engineering, electronic engineering, information engineering
RGB color model
020201 artificial intelligence & image processing
Artificial intelligence
business
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2018 4th International Conference on Computing Communication and Automation (ICCCA)
- Accession number :
- edsair.doi...........edde0c2e6a2ba62d67b6cc09ce545584