Facial Expression Analysis Based on High Dimensional Binary Features

High dimensional engineered features have yielded high performance results on a variety of visual recognition tasks and attracted significant recent attention. Here, we examine the problem of expression recognition in static facial images. We first present a technique to build high dimensional,

$$\sim 60\mathrm{k}$$

features composed of dense Census transformed vectors based on locations defined by facial keypoint predictions. The approach yields state of the art performance at 96.8% accuracy for detecting facial expressions on the well known Cohn-Kanade plus (CK+) evaluation and 93.2% for smile detection on the GENKI dataset. We also find that the subsequent application of a linear discriminative dimensionality reduction technique can make the approach more robust when keypoint locations are less precise. We go on to explore the recognition of expressions captured under more challenging pose and illumination conditions. Specifically, we test this representation on the GENKI smile detection dataset. Our high dimensional feature technique yields state of the art performance on both of these well known evaluations.