Comparison of Local Binary Pattern and Eigenfaces for Predict Suspect Positive Drugs

Priambodo, Bagus; Jumaryadi, Yuwan; Putra, Zico Pratama

doi:10.1109/bcwsp50066.2020.9249405

Cited by 2 publications

(3 citation statements)

References 12 publications

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“…However, simulations described in [9] revealed that the LDA method outperformed the PCA methodology. When the dimensionality of face images is high, LDA does inapplicable, so its advantage of identifying compelling features for class separability is lost.…”

Section: Introductionmentioning

confidence: 99%

Performance Comparison of Feature Extraction and Machine Learning Classification Algorithms for Face Recognition

Hadiprakoso¹,

Buana²

2021

ijics

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Face recognition is a highly active research topic in pattern recognition and computer vision, with numerous practical applications. Face recognition can provide the most natural interaction experience similar to the way humans can recognize others. This paper presents a performance comparison of various machine learning approaches and feature extraction algorithms. The feature extraction algorithm used is Principal Component Analysis (PCA), Latent Dirichlet Allocation (LDA), and a combination of PCA-LDA. The method used is to take a dataset sample and then evaluate and compare machine learning algorithms to analyze accuracy in recognizing faces. We also use feature extraction techniques on facial image capture to speed up data processing. The classification algorithms measured are k-nearest neighbor, naive Bayes, support vector machine, random forest, and gradient boosting. The results showed that the random forest classification algorithm was the most accurate face recognition method. On the other hand, the PCA-LDA combined feature extraction algorithm has lower false-negative and false-positive rates than PCA and LDA. In addition, the PCA feature extraction algorithm has the fastest performance in the process of recognizing faces

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Section: Introductionmentioning

confidence: 99%

Performance Comparison of Feature Extraction and Machine Learning Classification Algorithms for Face Recognition

Hadiprakoso¹,

Buana²

2021

ijics

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“…As a result, the body begins to respond to a decrease caused by a lack of proper nutrition. Persistent drug abuse also leads to severe skin damage due to hallucinations when users scratch their skin [2]. The face may appear unsightly with many pimples.…”

Section: Introductionmentioning

confidence: 99%

“…Further, the total faces are divided into five detection regions for feature computations and classification. Other studies use local binary patterns to extract local features to classify suspect drug users [2].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Drug Users Based on Facial Scratching Pattern

et al. 2021

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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%.

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Comparison of Local Binary Pattern and Eigenfaces for Predict Suspect Positive Drugs

Cited by 2 publications

References 12 publications

Performance Comparison of Feature Extraction and Machine Learning Classification Algorithms for Face Recognition

Performance Comparison of Feature Extraction and Machine Learning Classification Algorithms for Face Recognition

Prediction of Drug Users Based on Facial Scratching Pattern

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