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Facial expression recognition using the spectral graph wavelet

Facial expression recognition using the spectral graph wavelet

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The authors propose a method to recognise facial expressions based on graph signal processing (GSP) techniques. Facial expressions are characterised by local patterns in the facial regions such as eyes, lips etc. and interrelationships among them. A facial expression recognition algorithm needs to capture these variations in the facial regions at the local level and the interrelationships of these regions at the global level. Hence, in the authors’ opinion, GSP seems to be an appropriate tool for the purpose. In this study, a novel method is presented which makes use of graph signals to represent the facial regions. They leverage spectral graph wavelet transform to extract information for creating the feature descriptor. Here, different types of two-channel and three-channel filter banks have been used by setting the weights of their channels for finding the optimum performance of the recognition rate. Through simulation studies, it is observed that the use of Abspline filter bank provides the best result. The experimental investigations on the extended Cohn–Kanade (CK+) and the JAFFE datasets have been carried out and the results confirm the effectiveness of the proposed method in recognition rate improvement.

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