With the development of society, the proportion of the elderly population is increasing year by year, and population aging has become a global trend. As people grow older, the shape and texture features of the face will change to some extent, which increases the difficulty of face recognition. The key point to getting good performance in the elderly expression recognition task is how to capture the facial features effectively. In this paper, we use a Vision Transformer network and apply the Local Mutual Information Maximization (LMIM) technique to it. The correlation between the input map and the high-level hidden representation is evaluated through the optimization of local mutual information. This also allows for the identification of the most pertinent features between the input image and the hidden representation. Finally, we evaluated this method on three datasets, EFED(Self-constructed dataset on the elderly), FER2013, and Seeprettyface as benchmarks, and the experiments show that the experimental results after the addition of local mutual information maximization (LMIM) are improved by about 4% to 9% over the original.

Nowadays, with the rapid development of B2C e-commerce and the popularity of online shopping, the Web storages huge number of product reviews comment by customers. A large number of reviews made it difficult for manufacturers or potential customers to track the comments and suggestions that customers made. This paper presents a method for extracting emotional elements containing emotional objects and emotional words and their tendencies from product reviews based on mixed model. First we constructed conditional random fields to extract emotional elements, lead-in semantic and word meaning as features to improve the robustness of feature template and used rules for hierarchical filtering errors. Then we constructed support vector machine to classify the emotional tendency of the fine-grained elements to achieve key information from product reviews. Deep semantic information imported based on neural network to improve the traditional bag of word model. Experimental results show that the proposed model with deep features efficiently improved the F-Measure.

Mouse-cursor tracking, a new action-based measure of behavior, has emerged as one of the promising applications of affective computing. As facial expressions, gaits, electroencephalogram (EEG), and electrodermal activity (EDA) inform the emotions of computer users, the movement of the computer mouse-cursor reveals when people feel anxious, relaxed, attentive, joyful, and sad. However, the mouse tracking analysis has not previously been subject to systematic investigations of psychometric properties. The choice of motor features, experimental manipulations, and data transformation methods is ad hoc. In this study, we evaluate the impact of psychological factors on mouse-based affective computing and propose simple scoring algorithms that incorporate psychometric features such as the frame of reference, habituation, and measurement error. Our results demonstrate that our new dimensionality reduction method, merged PCA, outperforms conventional procedures, improving prediction performance by about 15 − 30%.