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Facial expression provides abundant information for social interaction, and the analysis and utilization of facial expression data are playing a huge driving role in all areas of society. Facial expression data can reflect people's mental state. In health care, the analysis and processing of facial expression data can promote the improvement of people's health. This paper introduces several important public facial expression databases and describes the process of facial expression recognition. The standard facial expression database FER2013 and CK+ were used as the main training samples. At the same time, the facial expression image data of 16 Chinese children were collected as supplementary samples. With the help of VGG19 and Resnet18 algorithm models of deep convolution neural network, this paper studies and develops an information system for the diagnosis of autism by facial expression data.

The facial expression data of the training samples are based on the standard expression database FER2013 and CK+. FER2013 and CK+ databases are a common facial expression data set, which is suitable for the research of facial expression recognition. On the basis of FER2013 and CK+ facial expression database, this paper uses the machine learning model support vector machine (SVM) and deep convolution neural network model CNN, VGG19 and Resnet18 to complete the facial expression recognition.

In this study, ten normal children and ten autistic patients were recruited to test the accuracy of the information system and the diagnostic effect of autism. After testing, the accuracy rate of facial expression recognition is 81.4 percent. This information system can easily identify autistic children. The feasibility of recognizing autism through facial expression is verified.

The CK+ facial expression database contains some adult facial expression images. In order to improve the accuracy of facial expression recognition for children, more facial expression data of children will be collected as training samples. Therefore, the recognition rate of the information system will be further improved.

This research uses facial expression data and the latest artificial intelligence technology, which is advanced in technology. The diagnostic accuracy of autism is higher than that of traditional systems, so this study is innovative. Research topics come from the actual needs of doctors, and the contents and methods of research have been discussed with doctors many times. The system can diagnose autism as early as possible, promote the early treatment and rehabilitation of patients, and then reduce the economic and mental burden of patients. Therefore, this information system has good social benefits and application value.

This study proposes a collaborative federated learning (CFL) framework to address personal data transmission and retention issues for artificial intelligence (AI)-enabled video surveillance in public spaces.

This study examines specific challenges for long-term people monitoring in public spaces and defines AI-enabled video surveillance requirements. Based on the requirements, this study proposes a CFL framework to gradually adapt AI models’ knowledge while reducing personal data transmission and retention. The framework uses three different federated learning strategies to rapidly learn from different new data sources while minimizing personal data transmission and retention to a central machine.

The findings confirm that the proposed CFL framework can help minimize the use of personal data without compromising the AI model's performance. The gradual learning strategies help develop AI-enabled video surveillance that continuously adapts for long-term deployment in public spaces.

This study makes two specific contributions to advance the development of AI-enabled video surveillance in public spaces. First, it examines specific challenges for long-term people monitoring in public spaces and defines AI-enabled video surveillance requirements. Second, it proposes a CFL framework to minimize data transmission and retention for AI-enabled video surveillance. The study provides comprehensive experimental results to evaluate the effectiveness of the proposed framework in the context of facial expression recognition (FER) which involves large-scale datasets.

This study aims to provide a critical reflection of the application of image recognition techniques in visual information mining in hospitality and tourism.

This study begins by reviewing the progress of image recognition and advantages of convolutional neural network-based image recognition models. Next, this study explains and exemplifies the mechanisms and functions of two relevant image recognition applications: object recognition and facial recognition. This study concludes by providing theoretical and practical implications and potential directions for future research.

After this study presents different potential applications and compares the use of image recognition with traditional manual methods, the main findings of this critical reflection revolve around the feasibility of the described techniques.

Knowledge on how to extract valuable visual information from large-scale user-generated photos to infer the online behavior of consumers and service providers and its influence on purchase decisions and firm performance is crucial to business practices in hospitality and tourism.

Visual information plays a crucial role in online travel agencies and peer-to-peer accommodation platforms from the side of sellers and buyers. However, extant studies relied heavily on traditional manual identification with small samples and subjective judgment. With the development of deep learning and computer vision techniques, current studies were able to extract various types of visual information from large-scale datasets with high accuracy and efficiency. To the best of the authors’ knowledge, this study is the first to offer an outlook of image recognition techniques for mining visual information in hospitality and tourism.

This paper primarily aims to focus on a review of convolutional neural network (CNN)-based eye control systems. The performance of CNNs in big data has led to the development of eye control systems. Therefore, a review of eye control systems based on CNNs is helpful for future research.

In this paper, first, it covers the fundamentals of the eye control system as well as the fundamentals of CNNs. Second, the standard CNN model and the target detection model are summarized. The eye control system’s CNN gaze estimation approach and model are next described and summarized. Finally, the progress of the gaze estimation of the eye control system is discussed and anticipated.

The eye control system accomplishes the control effect using gaze estimation technology, which focuses on the features and information of the eyeball, eye movement and gaze, among other things. The traditional eye control system adopts pupil monitoring, pupil positioning, Hough algorithm and other methods. This study will focus on a CNN-based eye control system. First of all, the authors present the CNN model, which is effective in image identification, target detection and tracking. Furthermore, the CNN-based eye control system is separated into three categories: semantic information, monocular/binocular and full-face. Finally, three challenges linked to the development of an eye control system based on a CNN are discussed, along with possible solutions.

This research can provide theoretical and engineering basis for the eye control system platform. In addition, it also summarizes the ideas of predecessors to support the development of future research.