  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.
