Most previous work of facial action recognition focused only on verifying whether a certain facial action unit appeared or not on a face image. In this paper, we report our investigation on the semantic relationships of facial action units and introduce a novel method for facial action unit recognition based on action unit classifiers and a Bayes network called Facial Action Unit Association Network (FAUAN). Compared with other methods, the proposed method attempts to identify a set of facial action units of a face image simultaneously. We achieve this goal by three steps. At first, the histogram of oriented gradients (HOG) is extracted as features and after that, a Multi-Layer Perceptron (MLP) is trained for the preliminary detection of each individual facial action unit. At last, FAUAN fuses the responses of all the facial action unit classifiers to determine a best set of facial action units. The proposed method achieves a promising performance on the extended Cohn-Kanade Dataset. Experimental results also show that when the individual unit classifiers are not so good, the performance could improve by nearly 10% in some cases when FAUAN is used.
CITATION STYLE
Chen, J., Chen, Z., Chi, Z., & Fu, H. (2015). Recognition of facial action units with action unit classifiers and an association network. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9009, pp. 672–683). Springer Verlag. https://doi.org/10.1007/978-3-319-16631-5_49
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