Feature matching errors of different models.

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.</div

Face recognition accuracy of distinctive ways.

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.</div

Facial recognition flowchart in FaceNet network.

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.</div

Schematic diagram of P-Net, R-Net, O-Net models.

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.</div

System structure of smart libraries in universities.

System structure of smart libraries in universities.</p

Face recognition recall rates for different methods.

Face recognition recall rates for different methods.</p

Satisfaction of university teachers and students with various face recognition algorithms.

Satisfaction of university teachers and students with various face recognition algorithms.</p