Fatigue Detection Through Car Driver’s Face Using Boosting Local Binary Patterns

Fatigue Detection Through Car Driver’s Face Using Boosting Local Binary Patterns

fatigue detection; adaboost; boosting local binary patterns (boosting LBP); support vector machine (SVM)

The general population is concerned with traffic accidents. Driver fatigue is one of the leading causes of car accidents. Several
factors, including nighttime driving, sleep deprivation, alcohol consumption, driving on monotonous roads, and drowsy and
fatigue-inducing drugs, can contribute to fatigue. This study proposes a facial appearance-based driver fatigue detection
system. This is based on the assumption that facial features can be used to identify driver fatigue. We categorize driver
conditions into three groups: normal, talking, and yawning. In this study, we used Adaboost to propose Boosting Local Binary
Patterns (LBP) to improve the image features of fatigue drivers in the Support Vector Machine (SVM) model. The experimental
results indicate that the system's optimal performance achieves an accuracy value of 93.68%, a recall value of 94%, and a
precision value of 94%

Grandhys Setyo Utomo, Ema Rachmawati, Febryanti Sthevanie

http://jurnal.iaii.or.id

Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association

October 2023

Sri Wahyuni

ISSN Media Electronic: 2580-0760

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