What is it about?
Traditional and classical methods of facial expression recognition are mainly based on intensity image and are prone to be disturbed by illumination, poses, and disguise. We developed a robust facial expression recognition method using RGB-D images and multichannel features.
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Why is it important?
Based on image entropy and visual saliency, facial texture features are firstly extracted from RGB images and depth images to construct the Histogram of Oriented Gradient (HOG) descriptors. And then, we extract geometric features of RGB images using Active Appearance Model (AAM). Combining the HOG texture features with the AAM geometric feature, we build a robust multichannel feature vector for facial expression recognition. On this basis, an improved Support Vector Machine (SVM) algorithm, namely GS-SVM, is used to classify facial expression recognition. The proposed GS-SVM algorithm applies Grid Search method to optimize the best parameters for SVM classifier and estimate the accuracy of each parameter combination in specified range.
Perspectives
The proposed methods are tested and evaluated on the merged RGB-D database. Experimental results show that the proposed algorithm not only achieves a higher average recognition rate but also is robust to uncontrolled environments.
Prof. Linqin CAI
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This page is a summary of: Robust facial expression recognition using RGB-D images and multichannel features, Multimedia Tools and Applications, May 2018, Springer Science + Business Media,
DOI: 10.1007/s11042-018-5981-x.
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