警觉
计算机科学
生物识别
人工智能
公制(单位)
计算机视觉
面子(社会学概念)
模式识别(心理学)
医学
社会科学
运营管理
社会学
药理学
经济
作者
Federico M. Sukno,Sri-Kaushik Pavani,Constantine Butakoff,Alejandro F. Frangi
标识
DOI:10.1007/978-3-642-04667-4_4
摘要
Several studies have related the alertness of an individual to their eye-blinking patterns. Accurate and automatic quantification of eye-blinks can be of much use in monitoring people at jobs that require high degree of alertness, such as that of a driver of a vehicle. This paper presents a non-intrusive system based on facial biometrics techniques, to accurately detect and quantify eye-blinks. Given a video sequence from a standard camera, the proposed procedure can output blink frequencies and durations, as well as the PERCLOS metric, which is the percentage of the time the eyes are at least 80% closed. The proposed algorithm was tested on 360 videos of the AV@CAR database, which amount to approximately 95,000 frames of 20 different people. Validation of the results against manual annotations yielded very high accuracy in the estimation of blink frequency with encouraging results in the estimation of PERCLOS (average error of 0.39%) and blink duration (average error within 2 frames).
科研通智能强力驱动
Strongly Powered by AbleSci AI