Online and offline integration of smart educational monitoring system based on face recognition smart sign-in devices is studied in the paper. The face image analysis algorithm proposed by us is a data simplification analysis method that commonly used for feature extraction and dimensionality reduction. It can not only concentrate the useful information in multi band images into the new principal component images as few as possible, but also make these principal component images uncorrelated, thus greatly reducing the total amount of data. The application of artificial intelligence technology in the smart classroom check-in system will improve the efficiency of attendance check-in and facilitate teachers' management of the class. Hence, the combination of these 2 technologies have been studied. Through the testing, the performance is validated. 

Online and Offline Integration of Smart Educational Monitoring System based on Face Recognition Smart Sign-in Devices

/pdf/research-on-library-face-book-return-model-based-on-hybrid-jebyu73e.pdf

Research on Library Face Book Return Model Based on Hybrid PCA and Kernel Function

The continuous development of science and technology has led to the gradual digitization and intelligence of campus construction. To apply facial recognition technology to construct smart libraries in higher education, this study optimizes traditional facial recognition algorithm models. Firstly, a smart management system for university libraries is designed with facial recognition as the core, and secondly, the traditional FaceNet network is optimized. Combined with MobileNet, Attention mechanism, Receptive field module and Mish activation function, the improved multitask face recognition convolutional neural network is built and used in the construction of university smart library. The performance verification of the constructed model shows that the feature matching error value of the model in a stable state is only 0.04. The recognition accuracy in the dataset is as high as 99.05%, with a recognition error as low as 0.51%. The facial recognition model used in university smart libraries can achieve 97.6% teacher satisfaction and 96.8% student satisfaction. In summary, the facial recognition model constructed by this paper has good recognition performance and can provide effective technical support for the construction of smart libraries.

Construction of a smart face recognition model for university libraries based on FaceNet-MMAR algorithm

With the continuous progress of technology, facial recognition technology is widely used in various scenarios as a mature biometric technology. However, the accuracy of facial feature recognition has become a major challenge. This study proposes a face length feature and angle feature recognition method for digital libraries, targeting the recognition of different facial features. Firstly, an in-depth study is conducted on the architecture of facial action networks based on attention mechanisms to provide more accurate and comprehensive facial features. Secondly, a network architecture based on length and angle features of facial expressions, the expression recognition network is explored to improve the recognition rate of different expressions. Finally, an end-to-end network framework based on attention mechanism for facial feature points is constructed to improve the accuracy and stability of facial feature recognition network. To verify the effectiveness of the proposed method, experiments were conducted using the facial expression dataset FER-2013. The experimental results showed that the average recognition rate for the seven common expressions was 97.28% to 99.97%. The highest recognition rate for happiness and surprise was 99.97%, while the relatively low recognition rate for anger, fear, and neutrality was 97.18%. The data has verified that the research method can effectively recognize and distinguish different facial expressions, with high accuracy and robustness. The recognition method based on attention mechanism for facial feature points has effectively optimized the recognition process of facial length and angle features, significantly improving the stability of facial expression recognition, especially in complex environments, providing reliable technical support for digital libraries and other fields. This study aims to promote the development of facial recognition technology in digital libraries, improve the service quality and user experience of digital libraries. 

Facial length and angle feature recognition for digital libraries

Innovative Strategies for Intelligent Services in Smart Libraries in the Information Age Based on Linear Discriminant Analysis

Multi-factor joint normalisation for face recognition in the wild

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Multi-factor joint normalisation for face recognition in the wild