PROGRAM SEDERHANA SISTEM PENGENALAN WAJAH MENGGUNAKAN FUNGSI JARAK

Over the years, face recognition ones of biometric characteristic has attracted attention of many researchers and scientists. Many efforts has been done to built an automatic system capable of recognizing faces with several different techniques. The simple program for face recognition has been developed. The purpose of this program is to compare efectifity two simple distance functions namely Manhattan (L1) and Euclidean (L2) as classifier for face recognition system. In this research, system is trained and tested using dataset images of the five persons with different expresion faces. The results of our experiments show that the system could be more accurate from 95% for Manhattan distance function (L1) to be 100% for Euclidean distance function (L2).

An Automatic Identification System of Human Skin Irritation

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.