Eye contact detection via deep neural networks

Viral Parekh, Ramanathan Subramanian, C. V. Jawahar

Research output: Contribution to conference (non-published works)Abstract

4 Citations (Scopus)

Abstract

With the presence of ubiquitous devices in our daily lives, effectively capturing and managing user attention becomes a critical device requirement. While gaze-tracking is typically employed to determine the user’s focus of attention, gaze-lock detection to sense eye-contact with a device is proposed in[16]. This work proposes eye contact detection using deep neural networks, and makes the following contributions: (1) With a convolutional neural network (CNN) architecture, we achieve superior eye-contact detection performance as compared to [16] with minimal data pre-processing; our algorithm is furthermore validated on multiple datasets, (2) Gaze-lock detection is improved by combining head pose and eye-gaze information consistent with social attention literature, and (3) We demonstrate gaze-locking on an Android mobile platform via CNN model compression.

Original languageEnglish
Pages366-374
Number of pages9
DOIs
Publication statusPublished - 2017
Externally publishedYes
Event19th International Conference on Human-Computer Interaction, HCI International 2017 - Vancouver, Canada
Duration: 9 Jul 201714 Jul 2017

Conference

Conference19th International Conference on Human-Computer Interaction, HCI International 2017
CountryCanada
CityVancouver
Period9/07/1714/07/17

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