Susmitha Mohan and Manoj Phirke, HCL Technologies, India
Eye gaze estimation aims to find the point of gaze which is basically,” where we look”. Estimating the gaze point plays an important role in many applications with varying usage. Gaze estimation is used in automotive industry to ensure safety. In the field of retail shopping and online marketing gaze estimation is used to analyse the consumer’s interest and focus. Gaze estimation is also used for psychological tests and in healthcare for diagnosing some of the neurological disorders. This also has a significant role to play in the field to entertainment. There are multiple ways by which eye gaze estimation can be done. This paper is about a comparative study done on two of the popular methods for gaze estimation using eye features. An infra-red camera is used to capture data for this study. Method 1 tracks corneal reflection centre w.r.t the pupil centre and method 2 tracks the pupil centre w.r.t the eye centre to estimate gaze. There are advantages and disadvantages with both the methods which has been looked into. Choosing the right method for gaze estimation hence depends on the type of application, precision required and many other factors including environmental conditions. This paper can act as a reference for researchers working in the same field.
NLP-based Chatbot, Explainable Artificial Intelligence (XAI), Ontology graph, GPT-2, Transfer Learning.