Jomar Cristian Erandio - Academia.edu (original) (raw)

Jomar Cristian Erandio

Address: Binan, Calabarzon, Philippines

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Papers by Jomar Cristian Erandio

Research paper thumbnail of Driver Drowsiness Detection Based on Eye Movement and Yawning Using Facial Landmark Analysis

The drowsiness of the drivers can lead to road accidents. With the growth of computer vision and ... more The drowsiness of the drivers can lead to road accidents. With the growth of computer vision and image processing technology it is now feasible to investigate this problem. This study aims to maintain driver’s consciousness and detect drowsiness using a 300W dataset with the application of API Based Histogram of Oriented Gradients and Linear Support Vector Machine method. This enable face detection and classification, Random Forest Regression with 2 Node Splits for each tree for facial landmark, and Euclidean Distance points extraction for the eyes and mouth to detect drowsiness based on closing of the eyes and yawning. Our results show the model attained: i) a 91.67% accuracy rate performance in 0° degree camera angle, and ii) 93.33% accuracy in detecting drowsiness activity in both +45° to -45° camera angle, both in with 85% accuracy result in detecting both eye and mouth simultaneously.

Research paper thumbnail of Driver Drowsiness Detection Based on Eye Movement and Yawning Using Facial Landmark Analysis

The drowsiness of the drivers can lead to road accidents. With the growth of computer vision and ... more The drowsiness of the drivers can lead to road accidents. With the growth of computer vision and image processing technology it is now feasible to investigate this problem. This study aims to maintain driver’s consciousness and detect drowsiness using a 300W dataset with the application of API Based Histogram of Oriented Gradients and Linear Support Vector Machine method. This enable face detection and classification, Random Forest Regression with 2 Node Splits for each tree for facial landmark, and Euclidean Distance points extraction for the eyes and mouth to detect drowsiness based on closing of the eyes and yawning. Our results show the model attained: i) a 91.67% accuracy rate performance in 0° degree camera angle, and ii) 93.33% accuracy in detecting drowsiness activity in both +45° to -45° camera angle, both in with 85% accuracy result in detecting both eye and mouth simultaneously.

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