Oculum afficit: Ocular Affect Recognition

TL;DR

This paper proposes a novel method for recognizing human affect by analyzing ocular regions, addressing challenges of partial face visibility in portable device usage.

Contribution

It introduces a new approach focusing on ocular features for affect recognition, improving accuracy with partial face images in portable device contexts.

Findings

Effective affect inference from ocular regions.

Improved recognition accuracy with partial faces.

Applicable to portable device scenarios.

Abstract

Recognizing human affect and emotions is a problem that has a wide range of applications within both academia and industry. Affect and emotion recognition within computer vision primarily relies on images of faces. With the prevalence of portable devices (e.g. smart phones and/or smart glasses),acquiring user facial images requires focus, time, and precision. While existing systems work great for full frontal faces, they tend to not work so well with partial faces like those of the operator of the device when under use. Due to this, we propose a methodology in which we can accurately infer the overall affect of a person by looking at the ocular region of an individual.