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Automatic recognition of spontaneous versus posed (SVP) facial expressions has received widespread attention in recent years for its potential applications in friendly human machine interface. Most existing works of SVP facial expression recognition extract geometry-based features which heavily rely on accurate detection and tracking of facial feature points. In this paper, a novel approach is proposed to distinguish between spontaneous and posed smiles using discriminative completed LBP from three orthogonal planes, which is an appearance-based local spatial-temporal descriptor. The descriptor devotes to extracting most robust and discriminative patterns of interest. In addition, flexible facial subregion cropping, a spatial division method, is proposed taking into account different facial organ size of different people and filtering of redundant information. Besides, in the temporal domain, a new division method is also applied, which divides the smile process according to smile dynamics . Experiments on three benchmark databases and comparisons to the stateof-the-art methods validate the advantages of our approach, obtaining an accuracy rate of 91.40% .

Depression has become one of the most common mental illnesses in the world. For better prediction and diagnosis, methods of automatic depression recognition based on speech signal are constantly proposed and updated, with a transition from the early traditional methods based on hand-crafted features to the application of architectures of deep learning. This paper systematically and precisely outlines the most prominent and up-to-date research of automatic depression recognition by intelligent speech signal processing so far. Furthermore, methods for acoustic feature extraction, algorithms for classification and regression, as well as end to end deep models are investigated and analysed. Finally, general trends are summarised and key unresolved issues are identified to be considered in future studies of automatic speech depression recognition.

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