人眼注视点估计方法的研究
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摘要
对人眼注视点的估计是建立在机器视觉和图像处理基础上的研究课题。直接利用眼睛的运动进行交互具有直接、自然的优点。作为测量人类意识行为的有效工具,视线跟踪技术正逐渐受到神经认知学、心理学、工业工程、市场营销和计算机科学等诸多领域的普遍关注。本文在总结国内外研究成果的基础上,对注视点估计中涉及到的人脸人眼定位、特征参量提取、系统设计与搭建等一系列技术问题进行了研究,主要的创新点总结如下：
1、提出了一种基于自适应增强算法(AdaBoost)与粒子滤波(Particle Filter,PF)相结合的人脸定位方法,解决了复杂背景下的人脸定位问题。利用AdaBoost解决了PF需要人工给定目标初始位置的问题,同时利用PF解决了AdaBoost存在的误检和漏检的问题,实现了两种方法的有效互补。
2、提出了一种基于人脸图像的人眼定位方法。首先以全局的方式采用主动表观模型(AAM)对人脸的形状和纹理建模。然后利用MVLR (Multivariate Linear Regression)和ICIA (Inverse Compositional Image Alignment)不同的收敛特性,在参数拟合过程中先用MVLR估计全局位移参数,再用ICIA估计人脸形状参数,从而得到准确的收敛结果,最终实现了人眼的精确定位。
3、提出了一种基于两次多项式拟合的人眼注视点估计方法,首先利用第一次多项式拟合考察当注视点不变时,虹膜中心相对偏移与反射光斑坐标之间的函数关系；然后利用第二次多项式拟合考察当反射光斑的位置确定后,虹膜中心的相对偏移与注视点之间的映射关系。通过两次拟合实现了人眼注视点的有效估计。
4、设计并搭建了一套基于红外光源的人眼视线估计系统,并提出了一种测量辅助光源在系统中相对位置的标定方法,为高精度的注视点估计提供了可靠的硬件环境。
Gazing estimation is a research subject built upon machine vision and image processing. Interaction through eye movement is direct and natural. As an effective tool to measure human behavior of consciousness, eye gaze tracking technology has been widely concerned by researchers from a lot of fields such as cognitive neuroscience, psychology, industrial engineering, marketing and computer science. This dissertation addresses the issue of face detection, eye localization, feature extraction, system design, and system set up by summarizing the research fruits achieved both at home and abroad. The major content and contributions are as follows:
1. A face detection method is proposed based on the combination of Adaptive Boost (AdaBoost) and Particle Filter (PF), which provides a solution for face detection in complex background. With the help of AdaBoost, it is no longer a necessary condition to have manually given initial position of target as required by PF, and meanwhile PF overcomes the problems of false detection and missing detection that exist with AdaBoost method. So the combination of AdaBoost and PF produces an effective complementary effect.
2. A human eye localizaion method is proposed based on face image. Firstly, models of face shape and texture are built by active appearance model (AAM) globally. Then, taking advantage of different convergence characteristics of MVLR (Multivariate Linear Regression) and ICIA (Inverse Compositional Image Alignment), in the process of parameters fitting, the parameters of the global transform are estimated by MVLR and face shape parameters are estimated by ICIA, so that an accurate convergence result is obtained, whereby realizing a precise eye location finally.
3. An eye gaze estimation method is proposed based on twice polynomial fitting. The first polynomial fitting is used to study the functional relation between the relative offset of iris center and the coordinate of reflected light spot when gazing point is fixed. In the second polynomial fitting, study is made on the mapping relationship between the relative offset of iris center and gazing point when the position of reflected light spot is determined. Therefore, eye gazing point can be effectively estimated through twice polynomial fitting.
4. An eye gaze estimation system is designed and built based on infrared light source, and in the system calibration process, a method is suggested on how to measure and calibrate the relative position of auxiliary light source in the system, which provides a reliable hardware environment for an accurate eye gaze estimation.
1、提出了一种基于自适应增强算法(AdaBoost)与粒子滤波(Particle Filter,PF)相结合的人脸定位方法,解决了复杂背景下的人脸定位问题。利用AdaBoost解决了PF需要人工给定目标初始位置的问题,同时利用PF解决了AdaBoost存在的误检和漏检的问题,实现了两种方法的有效互补。
2、提出了一种基于人脸图像的人眼定位方法。首先以全局的方式采用主动表观模型(AAM)对人脸的形状和纹理建模。然后利用MVLR (Multivariate Linear Regression)和ICIA (Inverse Compositional Image Alignment)不同的收敛特性,在参数拟合过程中先用MVLR估计全局位移参数,再用ICIA估计人脸形状参数,从而得到准确的收敛结果,最终实现了人眼的精确定位。
3、提出了一种基于两次多项式拟合的人眼注视点估计方法,首先利用第一次多项式拟合考察当注视点不变时,虹膜中心相对偏移与反射光斑坐标之间的函数关系；然后利用第二次多项式拟合考察当反射光斑的位置确定后,虹膜中心的相对偏移与注视点之间的映射关系。通过两次拟合实现了人眼注视点的有效估计。
4、设计并搭建了一套基于红外光源的人眼视线估计系统,并提出了一种测量辅助光源在系统中相对位置的标定方法,为高精度的注视点估计提供了可靠的硬件环境。
Gazing estimation is a research subject built upon machine vision and image processing. Interaction through eye movement is direct and natural. As an effective tool to measure human behavior of consciousness, eye gaze tracking technology has been widely concerned by researchers from a lot of fields such as cognitive neuroscience, psychology, industrial engineering, marketing and computer science. This dissertation addresses the issue of face detection, eye localization, feature extraction, system design, and system set up by summarizing the research fruits achieved both at home and abroad. The major content and contributions are as follows:
1. A face detection method is proposed based on the combination of Adaptive Boost (AdaBoost) and Particle Filter (PF), which provides a solution for face detection in complex background. With the help of AdaBoost, it is no longer a necessary condition to have manually given initial position of target as required by PF, and meanwhile PF overcomes the problems of false detection and missing detection that exist with AdaBoost method. So the combination of AdaBoost and PF produces an effective complementary effect.
2. A human eye localizaion method is proposed based on face image. Firstly, models of face shape and texture are built by active appearance model (AAM) globally. Then, taking advantage of different convergence characteristics of MVLR (Multivariate Linear Regression) and ICIA (Inverse Compositional Image Alignment), in the process of parameters fitting, the parameters of the global transform are estimated by MVLR and face shape parameters are estimated by ICIA, so that an accurate convergence result is obtained, whereby realizing a precise eye location finally.
3. An eye gaze estimation method is proposed based on twice polynomial fitting. The first polynomial fitting is used to study the functional relation between the relative offset of iris center and the coordinate of reflected light spot when gazing point is fixed. In the second polynomial fitting, study is made on the mapping relationship between the relative offset of iris center and gazing point when the position of reflected light spot is determined. Therefore, eye gazing point can be effectively estimated through twice polynomial fitting.
4. An eye gaze estimation system is designed and built based on infrared light source, and in the system calibration process, a method is suggested on how to measure and calibrate the relative position of auxiliary light source in the system, which provides a reliable hardware environment for an accurate eye gaze estimation.
引文
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