# Design of a Binocular Pupil and Gaze Point Detection System Utilizing High Definition Images
###### Abstract  
This study proposes a novel binocular pupil and gaze detection system utilizing a remote full high definition (full HD) camera and employing LabVIEW. LabVIEW is inherently parallel and has fewer time-consuming algorithms. Many eye tracker applications are monocular and use low resolution cameras due to real-time image processing difficulties. We utilized the computer's direct access memory channel for rapid data transmission and processed full HD images with LabVIEW. Full HD images make easier determinations of center coordinates/sizes of pupil and corneal reflection. We modified the camera so that the camera sensor passed only infrared (IR) images. Glints were taken as reference points for region of interest (ROI) area selection of the eye region in the face image. A morphologic filter was applied for erosion of noise, and a weighted average technique was used for center detection. To test system accuracy with 11 participants, we produced a visual stimulus set up to analyze each eye's movement. Nonlinear mapping function was utilized for gaze estimation. Pupil size, pupil position, glint position and gaze point coordinates were obtained with free natural head movements in our system. This system also works at 2046 \(\times\) 1086 resolution at 40 frames per second. It is assumed that 280 frames per second for 640 \(\times\) 480 pixel images is the case. Experimental results show that the average gaze detection error for 11 participants was 0.76\({}^{\circ}\) for the left eye, 0.89\({}^{\circ}\) for right eye and 0.83\({}^{\circ}\) for the mean of two eyes. eye tracker; binocular pupil detection; gaze point; LabVIEW 1  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON]  
1 March 201  
A contemporary and widely-adopted method among traditional gaze tracking methods [7; 8; 9] is video image based pupil center-corneal reflection method (PCCR) which does not require physical contact with the user's eye area. As it is not intrusive, PCCR is considered to be the most improved gaze tracking technology in recent years [10; 11]. In PCCR methods, pupil center relative position with respect to corneal reflection which is called \"glint\" is the main point. Besides this, gaze estimation is performed by detecting the center of the pupil and corneal reflections created by illuminators. Consequently, gaze estimations are done via using single [12; 13] or multiple illuminator-based methods [14; 15; 16] in many eye tracking studies. Eye gaze tracking systems can employ visible or infrared (IR) light as an illuminator. When visible light is preferred, it is difficult to detect pupil center and distinguish the pupil from noisy reflections. On the other hand, IR light eases pupil detection by means of near infrared (NIR) cameras. Thus, many eye tracker applications use IR light with NIR cameras. According to the eye tracking processes, one or more NIR cameras are utilized to obtain 2D or 3D video images. Three-dimensional video image-based systems have higher accuracies but they have lower working frequencies. Eye gaze estimation of users can be carried out using lots of techniques. Eye trackers have been designed as wearable or as remote gaze tracking systems. In the form of a wearable system, an eye tracking camera is mounted on the user's head [17; 18]. In the remote system, visual perception is acquired by a remote camera system [19; 20]. This case is more convenient for the users. Recently, several video-based pupil detection algorithms such as the Ellipse selector (ElSe) [21], the Exclusive Curve Selector (ExCuSe) [22], and the Convolutional Neural Network (CNN) [23] have been proposed to estimate eye pupil at high detection rates and low computational costs. The Ellipse selector (ElSe) is based on ellipse evaluation of a filtered edge image and pupil contour validation. It is a robust, resource-saving approach that can be integrated in embedded architectures. ExCuSe analyses the images depending on reflections based on intensity histograms.