Facial expression recognition based on multi‐regional D–S evidences theory fusion

Abstract

To achieve decision‐level fusion of multi‐regional features and highlight the credibility of different regional evidences, a facial expression recognition method based on multi‐regional evidence fusion is proposed. A block histogram of gradient Gabor features in three regions, namely eyebrows, eyes, and mouth, is extracted from a facial image and regarded as evidence in expression classification. Then, category membership and regional contribution are solved with the region‐weighted semisupervised fuzzy c‐means clustering algorithm to construct initial basic probability assignment (BPA) and emphasize the importance of different evidences, respectively. The initial BPA of evidence is further reassigned by combining region contribution and evidence supportability to reduce evidential conflict. Finally, the final decision‐level fusion of multi‐regional evidences is obtained based on the Dempster–Shafer (D–S) combination rule. The experimental results for the Cohn–Kanade expression database show that the BPA construction method based on category‐membership degree and the reassignment strategy based on region contribution and evidence supportability improves the recognition rate and maintains good robustness for all types of expressions. Compared with existing decision‐level fusion strategies and classification methods, the proposed recognition framework based on D–S evidences theory has the advantages in recognition performance and reliability, particularly in increasing the recognition rate for expressions that are difficult to distinguish, such as fear, sadness, and disgust. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.