distributing expressional faces in 2-d emotional space
Facial expressions are often classified into one of several basic emotion categories. This categorical approach seems improper to treat faces with blended emotion, as well as hard to measure the intensity of an emotion. In this paper facial expressions are evaluated with dimensional approach of affect that was originally introduced by psycho-physiologic study. An expressional face can be represented as a point in a two-dimensional (2-D) emotional space characterized by arousal and valence factors. To link low-level face features with emotional factors, we propose a simple method that builds an emotional mapping by a coarse labeling on Cohn-Kanade database and a linear fitting on the labeled data. Our preliminary experimental result shows that the proposed emotional mapping can be used to visualize the distribution of affective content in a large face set and further retrieval expressional face images or relevant video shots by specifying a region in the 2-D emotional space.
emotional space facial expression analysis least square fitting image retrieval