Comparison of Local Binary Pattern and Eigenfaces for Predict Suspect Positive Drugs
Bagus Priambodo, Yuwan Jumaryadi, Zico Pratama Putra · 2020
The current activity of drug inspection is usually carried out at school or university. This procedure, however, is less effective and efficient, as the urine samples are taken randomly. In many cases, the suspect student is not present or escapes the urine or hair inspection. A predictive drug user is needed, where only students suspected of positive drug use are selected for a urine test. To handle this problem, we need a system to predict suspect positive drugs. The dataset is generated from online sources by collecting and pre-processing 30 images of people before and after drug. We compare two algorithm local binary pattern and Eigenfaces for predicting suspect positive drugs based on face images. The experiment shows that the result of the prediction using Local binary pattern is better than the prediction using Eigenfaces. However, a higher accuracy of prediction reaches only 75 %.