期刊
IEEE ACCESS
卷 5, 期 -, 页码 10871-10881出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2017.2712788
关键词
Smart cities; health monitoring; facial expression; CS-LBP; SVM; GMM; bandlet transform
Human facial expressions change with different states of health; therefore, a facial-expression recognition system can be beneficial to a healthcare framework. In this paper, a facial-expression recognition system is proposed to improve the service of the healthcare in a smart city. The proposed system applies a bandlet transform to a face image to extract sub-bands. Then, a weighted, center-symmetric local binary pattern is applied to each sub-band block by block. The CS-LBP histograms of the blocks are concatenated to produce a feature vector of the face image. An optional feature-selection technique selects the most dominant features, which are then fed into two classifiers: a Gaussian mixture model and a support vector machine. The scores of these classifiers are fused by weight to produce a confidence score, which is used to make decisions about the facial expression's type. Several experiments are performed using a large set of data to validate the proposed system. Experimental results show that the proposed system can recognize facial expressions with 99.95% accuracy.
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