Skin Color Classification Using Color Mapping Co-occurrence Matrix
Ghazali Osman, Muhammad Suzuri Hitam
Journal of Information Organization, Vol. 3, No. 1, 2013.
DOI: 
Link: http://www.dline.info/jio/fulltext/v3n1/2.pdf
Abstract:
This paper presents a new technique for region-based skin color classification using texture features. Texture features were extracted from a new introduced technique called Color Mapping Co-occurrence Matrix (CMCM). Thirteen Haralickâ€™s texture features were extracted from CMCM at four directions and were used as input parameters for two skin color classifiers namely stepwise Linear Discriminant Analysis (LDA) and Multi-Layer Perceptron (MLP) Neural Network. The performance indicator of each skin color classifier is measured based on true positive rate and false positive rate. The experimental results showed that the skin color classifier using input features from [RGB] CMCM at direction (1, 0o) provides the most superior performances as compared to other CMCMâ€™s directions for both classifiers. It can also be concluded that MLP performs slightly better in classification performance as compared to stepwise LDA.