More than a feeling: The MiFace framework for defining facial communication mappings

Facial expressions transmit a variety of social, grammatical, and affective signals. For technology to leverage this rich source of communication, tools that better model the breadth of information they convey are required. MiFace is a novel framework for creating expression lexicons that map signal values to parameterized facial muscle movements inferred by trained experts. The set of generally accepted expressions established in this way is limited to six basic displays of affect. In contrast, our approach generatively simulates muscle movements on a 3D avatar. By applying natural language processing techniques to crowdsourced free-response labels for the resulting images, we efficiently converge on an expression's value across signal categories. Two studies returned 218 discriminable facial expressions with 51 unique labels. The six basic emotions are included, but we additionally define such nuanced expressions as embarrassed, curious, and hopeful.