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To investigate an effective representation model for 3D facial expression recognition, this paper proposes a multi-feature tensor neighborhood preserving embedding (MFTNPE) method, which seeks various attribute features from raw textured shape scan models of 3D facial expression to construct a multi-feature tensor model. With MFTNPE method, we can project the model into a tensor subspace, explore the interactions and correlations among multimodal data and then extract discriminative low-dimensional tensor features for 3D facial expression recognition. In the study, we first introduce a tensor model based on multi-feature integration for 3D facial expression recognition. Via exploring the textural and geometric features of 3D facial expression data, an original multi-feature tensor model is constructed by stacking various attribute maps orderly. Second, we utilize the tensor completion approach as the scheme of data preprocessing to correct the original multi-feature tensor model. Due to the presence of imperfect data in the projected attribute maps, the tensor completion can predict the missing data in tensor by the trace norm of multi-feature tensor model along each mode. Third, we redefine a weighted matrix for use in the orthogonal-based tensor neighborhood preserving embedding algorithm. In the weighted matrix, the class information is used to guarantee the correct neighborhood relationships for multi-feature tensor samples. This effectively avoids the disturbances and confusions between similar samples while preserving spatial structure and inter-class information. Finally, we conduct extensive experiments on two 3D facial expression recognition datasets. The experimental results show that the proposed method has much better efficiency and performance compared with the state-of-the-art methods.INDEX TERMS 3D facial expression recognition, multi-feature, tensor decomposition, tensor representation, tensor neighborhood preserving embedding.

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Owing to low light intensity, the image detector is underexposure, the colour and contrast of scene images will be changed, so it is of great significance to study the image sharpening in a weak illumination environment. In this study, the causes of image blurring are firstly analysed by histogram equalisation (HE) and retinex theory. A method combined HE and multi-scale retinex theory with colour recovery is proposed. The experimental simulation results show that the image processing effect of this algorithm is the most obvious, the local details and dark areas are clearly visible, the colour is rich, and the visual effect is the best, which has a certain practical application value.