Prediction of Drug Users Based on Facial Scratching Pattern

Bagus Priambodo, Yuwan Jumaryadi, Sarwati Rahayu, Diky Firdaus, Muhammad Sobri, Zico Pratama Putra


The current practice of drug inspection is usually carried out at school or university. This procedure, however, is not effective and efficient, as the urine samples are taken randomly. In many cases, the drug-taking student is not present or evades the urine or hair inspection. A predictive drug user tool is needed, where only suspected student drug users are selected for a urine test. In general, drug abuse constantly causes terrible damage to the skin lesions Since they damage the skin during hallucinations due to the effects of drugs. The Grey Level of Occurrence Matrix (GLCM) is used in this study to discover the scratch pattern. Our proposed GLCM is evaluated with 104 images collected from the Internet. Training data is generated from 88 images of people before and after the drug was collected from the Internet, and we set 16 image faces to test the prediction.  The experiment shows that the prediction based on GLCM has better accuracy (81%) compared with the local binary pattern (LBP) which only reach up to 75%.


local binary pattern, grey level co-occurrence matrix, prediction of drug users, face recognition

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International Journal of Online and Biomedical Engineering (iJOE) – eISSN: 2626-8493
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