FACIAL EXPRESSION RECOGNITION USING RELATIONS DETERMINED BY CLASS-TO-CLASS COMPARISONS

摘要

Facial expressions are recognized using relations determined by class-to-class comparisons. In one example, descriptors are determined for each of a plurality of facial expression classes. Pair-wise facial expression class-to-class tasks are defined (502). A set of discriminative image patches are learned for each task (504) using labelled training images. Each image patch is a portion of an image. Differences in the learned image patches in each training image are determined for each task (506). A relation graph is defined for each image for each task using the determined differences (508). A final descriptor is determined for each image by stacking and concatenating the relation graphs for each task (510). Finally, the final descriptors of the images are fed into a training algorithm to learn a final facial expression model (512).