Eye Gaze Detection Based on Computational Visual Perception and Facial Landmarks

The pandemic situation in 2020 brought about a‘digitized new normal’and created various issues within the current education systems.One of the issues is the monitoring of students during online examination situations.A system to determine the student’s eye gazes during an examination can help to eradicate malpractices.In this work,we track the users’eye gazes by incorporating twelve facial landmarks around both eyes in conjunction with computer vision and the HAAR classifier.We aim to implement eye gaze detection by considering facial landmarks with two different Convolutional Neural Network(CNN)models,namely the AlexNet model and the VGG16 model.The proposed system outperforms the traditional eye gaze detection system which only uses computer vision and the HAAR classifier in several evaluation metric scores.The proposed system is accurate without the need for complex hardware.Therefore,it can be implemented in educational institutes for the fair conduct of examinations,as well as in other instances where eye gaze detection is required.

Pupil center detection with a single webcam for gaze tracking

This paper presents a user friendly approach to localize the pupil center with a single web camera.Several methods have been proposed to determine the coordinates of the pupil center in an image,but with practical limitations.The proposed method can track the user’s eye movements in real time under normal image resolution and lighting conditions using a regular webcam,without special equipment such as infrared illuminators.After the pre-processing steps used to deal with illumination variations,the pupil center is detected using iterative thresholding by applying geometric constraints.Experimental results show that robustness and speed in determining the pupil’s location in real time for users of various ethnicities,under various lighting conditions,at different distances from the webcam and with standard resolution images.

Eye contact and sociability data suggests that Australian dingoes were never domesticated

Dogs were the first animal to become domesticated by humans,and they represent a classic model system for unraveling the processes of domestication.We compare Australian dingo eye contact and socialization with Basenji and German Shepherd dog(GSD)breeds.Australian dingoes arrived in Australia 5,000–8,000 BP,and there is debate whether they were domesticated before their arrival.The Basenji represents a primitive breed that diverged from the remaining breeds early in the domestication process,while GSDs are a breed dog selected from existing domestic dogs in the late 1800s.We conducted a 4-phase study with unfamiliar and familiar investigators either sitting passively or actively calling each canid.We found 75%of dingoes made eye contact in each phase.In contrast,86%of Basenjis and 96%of GSDs made eye contact.Dingoes also exhibited shorter eye-gaze duration than breed dogs and did not respond to their name being called actively.Sociability,quantified as a canid coming within 1m of the experimenter,was lowest for dingoes and highest for GSDs.For sociability duration,dingoes spent less time within 1m of the experimenter than either breed dog.When compared with previous studies,these data show that the dingo is behaviorally intermediate between wild wolves and Basenji dogs and suggest that it was not domesticated before it arrived in Australia.However,it remains possible that the accumulation of mutations since colonization has obscured historical behaviors,and dingoes now exist in a feralized retamed cycle.Additional morphological and genetic data are required to resolve this conundrum.