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Micro-expression recognition (MER) is an interdisciplinary research task that has attracted attention. This is because MER can be relevant to multiple fields, such as computer vision, psychology, human-computer interaction, and social security. Because the scarcity of databases and difficulty in video semantics understanding, end-to-end MER still faces many challenges. In this study, we propose an MER framework with attention mechanism and region enhancement (MER-AMRE). Attention mechanisms are introduced to enhance the representation performance of the model, which can improve the recognition accuracy. Additionally, we use Euler video magnification in data preprocessing to enhance facial variation areas. AffectNet is leveraged to pretrain a facial region of interest (RoI) feature extractor with attention regions. Finally, we combine the facial RoI features with global facial features to recognize micro-expressions. Extensive experiments on two well-known micro-expression datasets, CASME II and SAMM, verified the robustness and generalization of the proposed MER-AMRE framework. [ABSTRACT FROM AUTHOR]

Personality analysis is widely used in occupational aptitude tests and entrance psychological tests. However, answering hundreds of questions at once seems to be a burden. Inspired by personality psychology, we propose a multimodal attention network with Category-based mean square error (CBMSE) for personality assessment. With this method, we can obtain information about one’s behaviour from his or her daily videos, including his or her gaze distribution, speech features, and facial expression changes, to accurately determine personality traits. In particular, we propose a new approach to implementing an attention mechanism based on the facial Region of No Interest (RoNI), which can achieve higher accuracy and reduce the number of network parameters. Simultaneously, we use CBMSE, a loss function with a higher penalty for the fuzzy boundary in personality assessment, to help the network distinguish boundary data. After effective data fusion, this method achieves an average prediction accuracy of 92.07%, which is higher than any other state-of-the-art model on the dataset of the ChaLearn Looking at People challenge in association with ECCV 2016. [ABSTRACT FROM AUTHOR]