An Automatic Identification System of Human Skin Irritation
Abstract: Quantitative
characterization of human skin irritation is important but it is difficult task
to be done. Recently, an identification of human skin is still doing manually.
Indeed, the identification of the human skin irritation sample can be very
subjective. The analysis of the skin irritation could be conducted using
biochemical test, but it is not simple. In this research, a new approach of an
automatic human skin identification system based on image pattern recognition
is developed to obtain a decision of sample test (whether it has irritation or
not). This system design was developed using the following features extraction:
gray level histogram (GLH) feature and texture gray level co-occurrence
matrices (GLCM). Meanwhile, for a classification process, using the following distance metric:
Manhattan distance and Euclidean distance, or learning vector quantization
neural network (LVQ-NN). The combination between feature extractor and classifier
methods proposed was used to evaluate the performance system. The experimental
results show that the best accuracy for 83.33% was obtained when design system
was implementated using GLH or GLCM features through LVQ-NN classifier.
Penulis: Abdul Fadlil
Kode Jurnal: jptkomputerdd100049
