Obtaining a valid facial expression recognition (FER) method is still a research hotspot in the artificial intelligence field. In this paper, we propose a multiparameter fusion feature space and decision voting-based classification for facial expression recognition. First, the parameter of the fusion feature space is determined according to the cross-validation recognition accuracy of the Multiscale Block Local Binary Pattern Uniform Histogram (MB-LBPUH) descriptor filtering over the training samples. According to the parameters, we build various fusion feature spaces by employing multiclass linear discriminant analysis (LDA). In these spaces, fusion features composed of MB-LBPUH and Histogram of Oriented Gradient (HOG) features are used to represent different facial expressions. Finally, to resolve the inconvenient classifiable pattern problem caused by similar expression classes, a nearest neighbor-based decision voting strategy is designed to predict the classification results. In experiments with the JAFFE, CK+, and TFEID datasets, the proposed model clearly outperformed existing algorithms.
Extracting effective features of expressions becomes a hot research topic, and a single feature pattern cannot reflect the diversity of expressions. Therefore, to obtain rich information feature data and raise the expression recognition performance, we propose a feature fusion model of multiple feature selection by the measure of the RV correlation coefficient. In the proposed feature fusion model, the feature patterns are firstly selected by RV correlation coefficient from various expression texture features. And then according to rank the values of the RV correlation coefficient, we build a CCA subspace and PCA subspace respectively to fuse selected features. Finally, a new facial expression feature presentation is constructed through weighting and combining the two fusion features from the subspaces. The new features are fed to SVM classifier for expression recognition. Experimental verification shows that our proposed model has a superior performance than the existing algorithms.
Classification of apple is an important link in postharvest commercialization processing. To realize the non-destructive, rapid and effective discrimination of apple fruits, the near infrared reflectance spectra of four varieties of apples were collected using near infrared spectroscopy, reduced by principal component analysis (PCA) and used to extract the discriminant information by linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), fuzzy discriminant analysis (FDA) and Foley-Sammon discriminant analysis. Finally k-nearest neighbor finished the classification. The classification results showed that FDA could extract the discriminant information of NIR spectra more effectively, and achieved the highest classification accuracy.
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