In recent years, convolutional neural networks (CNNs) have shown great potential in synthetic aperture radar (SAR) target recognition.
This paper proposes a new database of Houma Alliance Book ancient handwritten characters and a multi-modal fusion method to recognize ancient handwritten characters.
More specifically, the backbone network aims at extracting feature representations from different facial regions, RI module computing an adaptive weight from the region itself based on attention mechanism with respect to the unobstructedness and importance for suppressing the influence of occlusion, and RR module exploiting the progressive interactions among these regions by performing graph convolutions.
It aims to obtain salient and discriminative features for specific expressions and also predict expression by fusing the expression-specific features.
In this paper, we investigate the cross-database micro-expression recognition problem, where the training and testing samples are from two different micro-expression databases.
Then, we prove that group-based sparse coding is equivalent to the rank minimization problem, and thus the sparse coefficient of each group is measured by estimating the singular values of each group.
We fuse face, upperbody and scene information for robustness of GER against the challenging environments.
Group sparsity has shown great potential in various low-level vision tasks (e. g, image denoising, deblurring and inpainting).
For increasing the discrimination of micro-expressions, we propose a new feature selection based on Laplacian method to extract the discriminative information for facial micro-expression recognition.
For ME recognition, the performance of previous studies is low.