Automatic facial emotion recognition using weber local descriptor for e-Healthcare system

Abstract

For an e-Healthcare system, detecting the emotion of a patient is vital for initial assessment of the patient. This paper proposes an emotion recognition system from face using for an e-Healthcare system. For features, Weber local descriptors (WLD) are utilized. In the proposed system, a static facial image is subdivided into many blocks. A multi-scale WLD is applied to each of the blocks to obtain a WLD histogram for the image. The significant bins of the histogram are found by using Fisher discrimination ratio. These bin values represent the descriptors of the face. The face descriptors are then input to a support vector machine based classifier to recognition the emotion. Two publicly available databases are used in the experiments. Experimental results demonstrate that the proposed WLD based system achieves a very high recognition rate, where the highest recognition accuracy reaches up to 99.28 % in case of Cohn---Kanade database.