Facial image processing for sleepiness estimation

Descripción del Articulo

Sleepiness could cause different accidents at work and in our daily lives. Activities such as driving a car, handling heavy machinery, being in charge of controlling a satellite or monitoring a nuclear plant, are activities that require short visual and motor reaction times. These reactions could be...

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Detalles Bibliográficos
Autores: Vargas Cuentas, Natalia, Roman Gonzalez, Avid
Formato: objeto de conferencia
Fecha de Publicación:2017
Institución:Universidad de Ciencias y Humanidades
Repositorio:UCH-Institucional
Lenguaje:inglés
OAI Identifier:oai:repositorio.uch.edu.pe:uch/335
Enlace del recurso:http://repositorio.uch.edu.pe/handle/uch/335
http://dx.doi.org/10.1109/BIOSMART.2017.8095346
https://ieeexplore.ieee.org/document/8095346
Nivel de acceso:acceso embargado
Materia:Accidents
Eye protection
Highway accidents
Image processing
Machinery
Roads and streets
Accidents at work
Eye detection
Facial image processing
Facial recognition
Road traffic injuries
Sleepiness
Vehicular accidents
Face recognition
Descripción
Sumario:Sleepiness could cause different accidents at work and in our daily lives. Activities such as driving a car, handling heavy machinery, being in charge of controlling a satellite or monitoring a nuclear plant, are activities that require short visual and motor reaction times. These reactions could be affected by sleepiness. The highest number of these accidents is related to vehicular accidents. WHO statistics show that 1.2 million people worldwide die every year due to road crashes, 50 million people are injured, and more than 3,000 people die daily from road traffic injuries. The primary objective of the present work is to develop a tool based on techniques of artificial intelligence and image processing that can detect states of sleepiness in people whose work needs the greatest attention-to avoid accidents. The idea is to develop an algorithm that can be executed on a computer and through a webcam can record the face of the person or worker to analyze the video and through facial recognition techniques can identify the eyes of the person, and thus identify eyelashes that would be an indicator of sleepiness. It is expected to have an algorithm developed in MATLAB to detect drowsiness and provide some notification.
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