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. 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  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.
|Number of pages||9|
|Publication status||Published - 2017|
|Event||19th International Conference on Human-Computer Interaction, HCI International 2017 - Vancouver, Canada|
Duration: 9 Jul 2017 → 14 Jul 2017
|Conference||19th International Conference on Human-Computer Interaction, HCI International 2017|
|Period||9/07/17 → 14/07/17|