Oculum afficit: Ocular Affect Recognition
This work addresses the challenge of affect recognition for users of portable devices like smartphones and smart glasses, where acquiring full facial images is difficult, offering a domain-specific solution.
The paper tackles the problem of affect recognition from partial facial images, particularly the ocular region, by proposing a methodology that accurately infers a person's overall affect, addressing limitations of existing systems that require full frontal faces.
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.