# A Method of Free-Space Point-of-Regard Estimation Based on 3D Eye Model and Stereo Vision
## 2 Related Works  
Earlier gaze estimating framework proposed by [3] has already demonstrated the widely application and the framework of this technology. Due to recent advances in computer vision, video camera-based gaze estimation has been developed by many researchers [1]. Gaze tracking methods based on video camera can be divided into two types: model-based method and regression-based method. These two types of methods have been proved effective in different application environments. Usually, model-based method builds a 3D eye model to calculate the gaze direction according to different eye and facial features [4; 5; 6]. These methods have good accuracy, and also have a longer history. But they usually combine strict personal calibration processes, and some with high accuracy are very complicated. Corneal reflection-based methods are proved to be very successful in the past few years [7; 8; 9]. This kind of methods are usually with high accuracy, and the error of gaze direction can be lower than 1 degree [10], but the camera and the light source in these gaze tracking systems should be calibrated before utilizing. Moreover, some methods' structures are more complicated [11; 12], it is tedious to deploy these proposed gaze tracking systems. Removal of the environment light noise from pupil images and the recognition of eyelid occlusion are important for the application of eye tracking system [13], and also the research works of making personal calibration more simply [14; 15; 16]. Moreover, these methods can also support high accuracy and real-time processing. Remote camera-based gaze tracking devices have also attracted people's attention because of their wide range of applications, but the need for personal calibration process is a constraint on the spread of these devices. Regression-based methods have prospect to resolve this problem, so they have developed rapidly in recent years, and their generality has been verified in various HCI applications [17]. The research on the regression-based method mainly concentrates on improving the precision and simplifying the steps of personal calibration. Regression-based methods can be divided into two categories [18]: feature-based methods and appearance-based methods. Feature-based methods establish mappings between eye features and eye directions, such as pupils, reflective spots, and so on [19; 20]. Appearance-based methods establish mappings between eye appearances and eye directions [21; 22; 23]. Moreover, machine learning algorithms for regression-based gaze estimation have been introduced. Many studies apply different sensors to gaze tracking devices. RGB-D camera can capture facial images and output depth information of facial features for achieving gaze estimation [24; 25; 26]. Because of simplified calibration, commercial RGB-D cameras have good application prospects in remote-camera devices [27]. [PERSON] et al. proposed a line-of-sight estimation for low resolution images [28]. This work presents a framework for integrating current appearance-based strategies into the framework for comparison. In conclusion, depth camera-based gaze tracking technology or utilizing other sensors has attracted more attention. Head-mounted gaze tracking system can be deployed freely, and obtain clear eye images, its eye direction estimation is more accurate. These are the largest advantages of such devices. In addition, a glasses-style gaze tracking device is able to capture clear eye images [29]. However, it relies on other sensors or systems to measure head posture [30]. The structure of a new wearable device is based on pupil-glint vector and binocular eyeball model [31], although the application is reasonable, but the error compensation is difficult. The above listed gaze tracking techniques have good accuracy in gaze detection within specific environments, but there are still many shortcomings in free-space 3D PoR estimation.