Real-time eye blink and wink detection for object selection in HCI systems
Real-time eye blink and wink detection for object selection in HCI systems
复制标题
实时眨眼和眨眼检测，用于 HCI 系统中的对象选择
DOI：
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发表时间：
2018
影响因子：
2.9
通讯作者：
J. Singh
中科院分区：
文献类型：
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作者：
Hari Singh Dhillon;J. Singh
本文提出了一种实时检测三种类型眨眼的方法：眨眼（同时眨眼的两只眼睛），左和右眨眼。眨眼检测的过程分为四个部分，即摄像机获取的人脸图像中的人脸定位、眼睛对定位、基于光流技术的像素运动分析和眨眼分类。眨眼检测已经使用视频摄像机和MATLAB软件与图像处理和计算机视觉工具箱。该算法花费大约60 ms的时间来处理一帧，并且花费250 ms的时间来确认和分类检测到的眨眼。进行了一个实验，以评估所提出的方法，其中10个用户自愿参加的性能。所提出的方法的性能进行了测试，在两种照明条件下：自然照明条件和受控照明条件。此外，性能已经通过改变用户与相机的距离进行了测试。这里，观察到当在受控照明条件下使用并且用户坐在约0.5m的距离处时，系统给出最佳性能。所提出的方法的准确性已被发现是96%，92%和88%的检测眨眼，左眨眼和右眨眼，分别。在ZJU数据集上进行了测试，其精度、检测准确率和虚警率分别为94.11%、91.2%和1.54%。所提出的系统已被用于执行各种鼠标类似的功能，使用眨眼和眨眼进行评估。它在执行左键单击、双击和右键单击操作时的准确率分别为90%、80%和90%。
This paper presents an approach for real-time detection of three types of eye blinks: eye blink (blinking both eyes simultaneously), left and right winks. The process of blink detection has been divided into four parts viz. face localization in facial images acquired through a video camera, eye pair localization, pixels’ motion analysis using optical flow technique, and classification of eye blinks. Blink detection has been performed using a video camera and MATLAB software with image processing and computer vision toolbox. The algorithm takes about 60 ms time for processing a frame and 250 ms time for confirmation and classification of the detected blink. An experiment was conducted to evaluate the performance of the proposed approach in which 10 users voluntarily participated. The performance of the proposed method has been tested under two lighting conditions: natural lighting conditions and controlled lighting conditions. Also, the performance has been tested by varying the distance of the user from the camera. Here, it is observed that the system gives best performance when used under controlled lighting conditions and the user sitting at a distance of about 0.5 m. Accuracy of the proposed approach has been found to be 96, 92 and 88% for detection of eye blink, left wink and right wink, respectively. The proposed method has also been tested on ZJU dataset where it has given precision, detection accuracy and false alarm rate of values 94.11, 91.2 and 1.54%, respectively. The proposed system has been used and evaluated for performing various mouse analogous functions using eye blinks and winks. It has given an accuracy of 90, 80 and 90% in performing left click, double click, and right click operations, respectively.