Visual-Based Eye Contact Detection in Multi-Person Interactions

Visual non-verbal behavior analysis (VNBA) methods mainly depend on extracting an important and essential social cue, called eye contact, for performing a wide range of analysis such as dominant person detection. Besides the major need for an automated eye-contact detection method, existing state-of-the-art methods require intrusive devices for detecting any contacts at the eye-level. Also, such methods are completely dependent on supervised learning approaches to produce eye-contact classification models, raising the need for ground truth datasets. To overcome the limitations of existing techniques, we propose a novel geometrical method to detect eye contact in natural multi-person interactions without the need of any intrusive eye-tracking device. We have experimented our method on 10 social videos, each 20 minutes long. Experiments demonstrate highly competitive efficiency with regards to classification performance, compared to the classical existing supervised eye contact detection methods.