Action units classification using clusWISARD

Abstract

This paper presents the use of WiSARD and ClusWiSARD weightless neural networks models for the classification of the contraction and extension of Action Units, the facial muscles involved in emotive expressions. This is a complex problem due to the large number of very similar classes, and because it is a multi-label classification task, where the positive expression of one class can modify the response of the others. WiSARD and ClusWiSARD solutions are proposed and validated using the CK+ dataset, producing responses with accuracy of 89.66%. Some of the major works in the field are cited here, but a proper comparison is not possible due to a lack of appropriate information about such solutions, such as the subset of classes used and the time of training/testing. The contribution of this paper is in the pioneering use of weightless neural networks in an AUs classification task, in the unpublished application of the WiSARD and ClusWiSARD models in multi-label tasks and in the new unsupervised expansion of ClusWiSARD proposed here.