Facial expression recognition using radial encoding of local Gabor features and classifier synthesis

Primarily motivated by some characteristics of the human visual cortex (HVC), we propose a new facial expression recognition scheme, involving a statistical synthesis of hierarchical classifiers. In this scheme, the input images of the database are first subjected to local, multi-scale Gabor-filter operations, and then the resulting Gabor decompositions are encoded using radial grids, imitating the topographical map-structure of the HVC. The codes are fed to local classifiers to produce global features, representing facial expressions. Experimental results show that such a hybrid combination of the HVC structure with a hierarchical classifier significantly improves expression recognition accuracy when applied to wide-ranging databases in comparison with the results in the literature. Furthermore, the proposed system is not only robust to corrupted data and missing information, but can also be generalized to cross-database expression recognition.

Figure optionsDownload high-quality image (100 K)Download as PowerPoint slideHighlights
► A radial encoding strategy for efficiently downsampling Gabor filter outputs.
► A new classifier combination method by extracting information from local classifiers.
► Extraction of features to represent facial expression efficiently.
► Cross-database test for person-independent facial expression recognition.