Abstract
Facial expression has many applications in human-computer interaction. Although feature extraction and selection have been well studied, the specificity of each expression variation is not fully explored in state-of-the-art works. In this work, the problem of multiclass expression recognition is converted into triplet-wise expression recognition. For each expression triplet, a new feature optimization model based on action unit (AU) weighting and patch weight optimization is proposed to represent the specificity of the expression triplet. The sparse representation-based approach is then proposed to detect the active AUs of the testing sample for better generalization. The algorithm achieved competitive accuracies of 89.67% and 94.09% for the Jaffe and Cohn–Kanade (CK+) databases, respectively. Better cross-database performance has also been observed.
Highlights
IntroductionWith the help of facial expression recognition, human-computer interaction can automatically obtain the information of the human face and infer the psychological status of the user, which can be applied to driver monitoring, face paralysis expression recognition, intelligent access control, and so on
With the help of facial expression recognition, human-computer interaction can automatically obtain the information of the human face and infer the psychological status of the user, which can be applied to driver monitoring, face paralysis expression recognition, intelligent access control, and so on.Recognition of six basic expressions, like happy (Ha), angry (An), surprise (Su), fear (Fe), disgust (Di), sad (Sa) and neutral (Ne) expression, can be categorized into 3D-based and 2D-based approaches
A two-stage expression recognition model based on triplet-wise feature optimization is proposed; the novelty of the this work is concentrated on three aspects
Summary
With the help of facial expression recognition, human-computer interaction can automatically obtain the information of the human face and infer the psychological status of the user, which can be applied to driver monitoring, face paralysis expression recognition, intelligent access control, and so on. A unified classification system [41] integrating feature selection and reduction was proposed based on the boosted deep belief network (BDBN) These algorithms devised and selected the same features for all of the categories of expressions (such as six basic expressions), which may leave out the feature specialty and multi-scale property. They are not good for encoding the micro-scale feature, since the combination space of AUs is limited when they are not carefully organized Most of these algorithms learned the features from the training expression samples. The weights of patches contained in each AU are finely optimized to represent the characteristics of different expressions In this way, the advantages of large-scale (AU-based) and small-scale (patch-wise) features are both explored.
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