Eye Gaze Tracking for Detecting Non-verbal Communication in MeetingEnvironments

Naina Dhingra a, Christian Hirt b, Manuel Angst, Andreas Kunz c

Innovation Center Virtual Reality, ETH Zurich, Zurich, Switzerland{ndhingra, hirtc, kunz }@iwf.mavt.ethz.ch

Keywords: Eye gaze, Eye tracker, OpenFace, Machine Learning, Support Vector Machine, Regression, Data Processing

Abstract: Non-verbal communication in a team meeting is important to understand the essence of the conversation.Among other gestures, eye gaze shows the focus of interest on a common workspace and can also be usedfor an interpersonal synchronisation. If this non-verbal information is missing and or cannot be perceived byblind and visually impaired people (BVIP), they would lack important information to get fully immersed inthe meeting and may feel alienated in the course of the discussion. Thus, this paper proposes an automaticsystem to track where a sighted person is gazing at. We use the open source software ’OpenFace’ and developit as an eye tracker by using a support vector regressor to make it work similarly to commercially availableexpensive eye trackers. We calibrate OpenFace using a desktop screen with a 2×3 box matrix and conduct auser study with 28 users on a big screen (161.7 cm x 99.8 cm x 11.5 cm) with a 1×5 box matrix. In this userstudy, we compare the results of our developed algorithm for OpenFace to an SMI RED 250 eye tracker. Theresults showed that our work achieved an overall relative accuracy of 58.54%.

1 Introduction

One important factor of non-verbal communica-tion is that people are often looking at artifacts on thecommon work space or at the other person when col-laborating with each other. Eye gaze provides infor-mation on emotional state (Bal et al., 2010), text entry(Majaranta and Raiha, 2007), or concentration for anobject (Symons et al., 2004) given by the user, to in-fer visualization tasks and a user’s cognitive abilities(Steichen et al., 2013), to enhance interaction (Hen-nessey et al., 2014), to have communication via eyegaze patterns (Qvarfordt and Zhai, 2005), etc. How-ever, such information cannot be accessed by blindand visually impaired people (BVIP) as they cannotsee where the other person in the meeting room islooking at (Dhingra and Kunz, 2019; Dhingra et al.,2020). Therefore, it is important to track eye gaze inthe meeting environment to provide the relevant in-formation to them.

Eye gaze tracking is locating the position where aperson is looking at. This specific spatial position isknown as the point of gaze (O’Reilly et al., 2019). It

a https://orcid.org/0000-0001-7546-1213b https://orcid.org/0000-0003-4396-1496c https://orcid.org/0000-0002-6495-4327

has been employed for research in scan patterns andattention in human-computer interaction, as well as inpsychological analysis. Eye gaze tracking technologyis categorized into two categories, i.e., head-mountedsystems and remote systems where head-mounted eyetrackers are mobile and remote systems are stationarytrackers.

Early eye tracking systems were based on metalcontact lenses (Agarwal et al., 2019a), while today’seye trackers use an infrared camera and a bright ordark pupil technique (Duchowski, 2007). These tech-niques locate the pupil’s center. The tracker can thenlocate the target’s position on the screen where theperson is gazing at using the relative position of thecorneal reflection and the pupil center. Other eyetrackers which are based on high speed video camerasare more expensive than the infrared based eye track-ers, but are also more accurate than webcam based eyetrackers (Agarwal et al., 2019a). In such eye trackers,the measurement is done based on deep learning andcomputer vision applications (Kato et al., 2019), (Yiuet al., 2019).

Based on the possibility to improve the accessibil-ity of non-verbal communication for BVIP, our workwill use eye gaze tracking to detect where people arelooking at. Based on the availability and known ad-vantages of the systems, the OpenFace and SMI RED

250 were chosen to be included in the analysis. Open-Face is an open-source software for real-time faceembedding visualization and feature extraction thatworks with webcams. The commercialized SMI RED250 remote eye tracker comes with the iView softwareto process the data.

The main contributions of this work are as fol-lows: (1) We developed an overall low-cost eyetracker which uses a webcam and the OpenFace soft-ware together with support vector machines to im-prove accuracy; (2) we designed this eye tracking sys-tem to be used in real-time in meeting environments;(3) we performed a user study with 28 users to evalu-ate the performance of this new eye gaze tracking ap-proach; and (4) we evaluated the effect of users wear-ing glasses and not wearing glasses on performance.

The motivation of our work is the use of a lowcost webcam along with a free open source face fea-ture detection software, since commercial eye track-ing systems are usually not available in typical meet-ing rooms and are very expensive.

This paper is organized as follows: Section 2 de-scribes the state of the art in eye tracking. Section3 briefly describes the methods and techniques usedin our system, while Section 4 gives details aboutthe experimental setup. In section 5, we show anoverview of the conducted user study and discussesthe achieved results. Finally, Section 6 concludes thepaper with future work and improvements of the cur-rent system.

2 State of the Art

The advancement of computers and peripheralhardware led to several different applications whichare based on gaze interactions. These applicationscan be divided into various sub-categories: TV panels(Lee et al., 2010), head-mounted displays (Ryan et al.,2008), automotive setups (Ji and Yang, 2002), desk-top computers (Dong et al., 2015), (Pi and Shi, 2017),and hand-held devices (Nagamatsu et al., 2010). Nu-merous researchers have worked on other eye gaze in-teractions such as public and large displays. (Drewesand Schmidt, 2007) worked on eye movements andgaze gestures for public display application. Anotherwork by (Zhang et al., 2013) built a system for detect-ing eye gaze gestures to the right and left directions.In such systems, either hardware-based or software-based eye tracking is employed.

2.1 Hardware-based Eye Gaze TrackingSystems

Hardware-based eye gaze trackers are commerciallyavailable and usually provide high accuracy thatcomes with a high cost of such devices. Such eye gazetrackers can further be categorized into two groups,i.e., head-mounted eye trackers and remote eye track-ers. Head-mounted devices usually consist of a num-ber of cameras and near-infrared (NIR) light emittingdiodes (Eivazi et al., 2018), being integrated in theframe of goggles. Remote eye trackers on the otherhand are stationary. We used an SMI RED 250 eyetracker which is one of the remote eye trackers. Usingthis device, we built an automatic eye gaze trackingsystem for users sitting in the meeting environment.

2.2 Software-based Eye Gaze TrackingSystems

Software-based eye gaze tracking uses features ex-tracted from a regular camera image by computer vi-sion algorithms. In (Zhu and Yang, 2002), the cen-ter of the iris is identified using an interpolated Sobeledge detection. Head direction also plays a signif-icant role in the eye gaze tracking, e.g. in (Valentiet al., 2011) where a combination of eye location andhead pose is used. In (Torricelli et al., 2008), a generalregression neural network (GRNN) is used to map ge-ometric features of the eye position to screen coordi-nates. The accuracy of GRNN is depending on theinput vectors. A low cost eye gaze tracking system isdeveloped in (Ince and Kim, 2011) using the eye pupilcenter detection and movement. It performed well ona low resolution video but had the drawback of be-ing dependant on head movement and pose. Webcam-based gaze tracking has also been researched in sev-eral different works using computer vision techniques(Dostal et al., 2013; Agarwal et al., 2019b). Theseworks implemented the feature detection in a propri-etary way, whereas we are implementing our systemwith an open source software which detects featurewith good accuracy and we tuned the mapping fromdetected features to screen coordinates based on acommercially available eye tracker using support vec-tor machine regression.

3 Methodology

3.1 OpenFace

OpenFace (Baltrusaitis et al., 2016) is an open-sourcesoftware which can be used in real-time for analyz-ing facial features. The software has various features:facial landmark detection (Amos et al., 2016), faciallandmark and head pose tracking, eye gaze tracking,facial action unit detection, behavior analysis (Bal-trusaitis et al., 2018), etc. We used OpenFaceOf-fline out of various applications available in the Open-Face package. OpenFace can analyze videos, images,image sequences, and live webcam videos. Gazerecordings include gaze directions for both eyes sep-arately, an averaged gaze angle, and eye landmarksin two-dimensional image coordinates and in three-dimensional coordinates of the camera’s coordinatesystem. Additionally, the timestamp and the successrate are recorded automatically.

3.2 Eye tracker SMI RED 250 withiView

The SMI RED 250 comes with the modular designwhich can be integrated into numerous configurationsranging from a small desktop screen to big televi-sion screens or projectors. It utilizes head movementand eye tracking along with the pupil and gaze datato achieve accurate results. SMI claims to have ro-bust results regardless of the age of the user, glasses,lenses, eye color, etc. The system needs to be cali-brated which takes a few seconds and maintains its ac-curacy during the duration of the experiments. It cantrack eye gaze up to 40 degrees in horizontal directionand 60 degrees in vertical direction. iView softwareis provided by SMI along with the eye tracker for thedata output and processing.

3.3 Support Vector Machine Regression

Support vector machines (SVMs) were developed asa binary classification algorithm to increase the gapbetween different categories or classes from the train-ing set (Suykens and Vandewalle, 1999). SVMs arealso used as a regression tool with an intuition to buildhyperplanes which are as close as possible to train-ing examples. For more detailed mathematical in-formation on SVMs for regression, refer to (Smolaand Scholkopf, 2004). In our system, we will useSVMs to assign measurement values to the predefinedclasses although such data might not precisely matchthe training data.

Figure 1: Setup for the small desktop screen.

3.4 Basic Pipeline

In the experiments, we used the SMI eye tracker withthe iView software and compared it to a regular we-bcam together with the open-source software Open-Face with some adjustments to their output using theaforementioned SVMs. Since the output from Open-Face was given in terms of eye position and eye gazedirection, we used a mathematical and geometricalmanipulation to convert these given vectors to screencoordinates.

For the comparison, two different setups wereused: a 22” monitor at 0.6m distance (see Figure 1),and a 65” screen at a distance of 3m to the user (seeFigure 4). We also performed a user study with 28users on this 65” screen. In both cases, the SMI eyetracker and the webcam were placed at a distance of0.6m to the user. These two different setups were usedto prove the robustness of our system as well as tovalidate our algorithm. It also evaluates that this sys-tem can be employed in meeting environments wheresighted people might look at two screens placed at dif-ferent distances having different measurement noise.

3.5 Desktop/Small Screen Setup

The setup used for the experiments with the desk-top screen is shown in Figure 1. The user had tolook at different regions on the desktop screen whilehis eye gaze was measured by the commercial eyegaze tracker (using iView) as well as by the the we-bcam (using OpenFace without any correction algo-rithm). The experiments with this setup showed thatthe screen coordinates from OpenFace are accumu-lated at a sub-region of the whole screen as shown inFigure 5.

Because of the accumulation of points, we usedan SVM algorithm to convert those coordinates into asimilar form as the output coordinates from the iViewsoftware. Figure 6 shows the scatterplot for iView and

OpenFace coordinates after using the SVM algorithmfor correction. We used 70% of the data for trainingand 30% of the data for testing.

We used a 2× 3 matrix as shown in Figure 2 toevaluate the manipulated output from OpenFace andSMI RED 250 in terms of 6 classes. The user wastold to look at the numbered fields of this matrix andhis eye gaze was simultaneously measured using thewebcam with OpenFace and using the SMI RED 250with iView. The measurements stemming from theSMI RED 250 were taken as ground truth. Figure 3shows the accuracy per box for the 2×3 matrix. It isevident that OpenFace performs better for the middleboxes than for the boxes 4 and 6. The results are alsoshown in Table 1. We achieved an accuracy of 57.69%on the test data using the SVM algorithm for regress-ing points from OpenFace to iView coordinates.

Figure 2: 2×3 matrix for the comparison of OpenFace andiView after applying the correction with the SVM.

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Figure 3: Accuracy per box for OpenFace (with SVM ap-plied) when the iView box number is considered as groundtruth.

Table 1: The accuracy of OpenFace with SVM using theSMI RED 250 eye tracker as reference. Values are in %.

Box Number OpenFace with SVMOverall 57.691 62.822 65.923 75.364 35.845 60.476 47.54

4 Experimental Setup for UserStudy

The setup consists of a demo environment wherea sighted person is looking at a screen. We are aimingto provide the useful information to the BVIP aboutthe location of the sub-region of the screen where aperson in the meeting is looking at. We used 1 ×5 boxes in the matrix shown to the user at the timeduring the user study. In our application, we are con-cerned about the region of interest of the person gaz-ing at the screen, but not in the particular location.Accordingly, we assume that the screen is divided into5 sub-parts and aim to provide high accuracy for pre-dicting the sub-part of interest of the user. The exper-imental setup is shown in 4.

The user was asked to look at a numbered regionfor few seconds and then the next region number wasgiven. The sequence of region numbers a user had tolook at was the same for every user to keep the unifor-mity. The SMI RED 250 eye-tracker with iView andOpenFace were used to take measurements simulta-neously. The data was refined and processed for thesample values which had the same time stamp.

Figure 4: Experimental setup for user study.

5 User Study and Results

We conducted the user study with 28 users whichhad a mix of people with and without spectacles orlenses. We used the SMART Board R© 400 series in-teractive overlay flat-panel display named as SBID-L465-MP. It has a 65” screen diagonal and its dimen-sions are: 161.7 cm × 99.8 cm × 11.5 cm. TheSMI RED 250 eye-tracker with iView and the web-cam with OpenFace were used to take measurementssimultaneously. We used the same approach as forthe small desktop screen. But in this case instead oftraining the OpenFace points to perform similar to theiView points, we train to compare the performance us-ing the ground truth given by the known positions ofthe 5 different fields. We used the SVM algorithm toperform a spatial manipulation of the data so that theaccumulated OpenFace data are similar to the iViewdata as shown in Figures 5 and 6.

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Figure 5: Scatter plots showing the output data from iViewand OpenFace. It is shown that the output from the Open-Face is concentrated in the certain area of the the screen.

We asked each user to sit in front of the big screenat a distance of 3 meters. The screen displayed a ma-trix of 1×5 numbers. The users were asked to look atthose numbers as shown in Figure 7. Whenever a userlooked at some number, that region was highlighted ingreen. We gave the same sequence of numbers to all28 users to look at. Then, we saved the raw data fromboth software, i.e., iView and OpenFace.

We analyzed the raw data to know whether themeasured position coincides with the original posi-tion of the corresponding box number on the screen.We compared the output results from both software atthe same time stamps with the ground truth numbersat which the user was asked to look at for a particu-

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Figure 6: Support vector machines algorithm is used toregress the data in a way so that OpenFace produces resultssimilar to iView.

Figure 7: Numbers displayed on the big screen at a distanceof 3m from the user.

lar time stamp. The average accuracy for iView was81.48% and for the OpenFace with SVM was 58.54%.

We further analyzed the accuracy of each box ofa matrix as shown in Table 2. We see that the ac-curacy for the box number 1 and 5 is the lowest forOpenFace, which means that it is unable to recognizecorrectly for the eye gaze at the corner boxes, whileit performs better for the boxes in the middle. Figure8 describes the performance comparison of our setup

Table 2: Accuracy of the SMI RED 250 eye tracker com-pared to OpenFace with SVM.

Box Number SMI RED 250 OpenFace with SVMOverall 81.48 58.541 84.03 42.252 81.72 62.893 78.85 67.464 81.04 70.415 81.74 49.71

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Figure 8: Accuracy per box for all the users.

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Figure 9: Number of successful detections for the test datain user study.

which shows that there is a potential of the open-source software such as OpenFace to work as an eyegaze tracker. Figure 9 shows the number of hits foreach out of 5 boxes, which further tells us that thecorner boxes, i.e., box number 1 and 5 have lowestnumber of hits which is in accordance to the accuracyachieved per box as shown in Figure 8.

Figure 10 shows the accuracy of each of the 28users in our user study. This shows that for certainusers our system outperformed the iView SMI RED250 eye tracker.

We also evaluated the results for the users wearing

Table 3: The accuracy of the SMI RED 250 eye trackercompared to OpenFace with SVM for users with and with-out glasses.

SMI RED 250 OpenFace with SVMglasses 75.02 50.90no glasses 85.96 66.79

glasses and without glasses as shown in Figure 11 andFigure 12. It is evident that both systems work betterwith the user without glasses compared to users withglasses. Table 3 details the accuracy for the comparedsystems for users with and without glasses.

6 Conclusion

We worked on the eye gaze location detection onthe screen for team meetings to help BVIP to get im-mersed in the conversation. We built a prototype ofan automatic eye gaze tracking system which can beavailable at low cost using an open source software’OpenFace’. We geometrically converted the eye gazevectors and eye position coordinates to screen coor-dinates and manipulated those coordinates using anSVM regression algorithm to work in a similar man-ner to the commercially available SMI 250 RED eyetracker. We used a small desktop screen with 2× 3box matrix to calibrate our proposed system for eyegaze tracking.

In our user study, we evaluated our automatic sys-tem with 28 users. We found out that our systemworks quite comparable to the SMI RED 250 eyetracker for the numbers which are inside the box ma-trix on the screen but inversely for the corner boxeson the screen which led to the accuracy difference be-tween the proposed system and the SMI RED 250.We compared the performance of users with spec-tacles and without spectacles which showed that theusers with spectacles had less accuracy than withoutspectacles which might be due to the extra reflectiondue to presence glasses.

In future work, we will convert the output in sucha way that it can be made accessible to BVIP by au-dio or haptic feedback. We will also work on improv-ing the accuracy of our system using neural networkswhich have proven to perform better than the classicalcomputer vision techniques in other problems.

ACKNOWLEDGEMENTS

This work has been supported by the Swiss Na-tional Science Foundation (SNF) under the grant no.200021E 177542 / 1. It is part of a joint project be-tween TU Darmstadt, ETH Zurich, and JKU Linzwith the respective funding organizations DFG (Ger-man Research Foundation), SNF and FWF (AustrianScience Fund). We also thank Dr. Quentin Lohmeyerand Product Development Group Zurich for lendingus the SMI RED 250 eye tracker.

Figure 10: Accuracy per user for the big screen.

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Figure 11: Accuracy per box for users with glasses.

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