Library, Yantai Vocational College, Yantai, China.
PLoS One. 2024 Jan 11;19(1):e0296656. doi: 10.1371/journal.pone.0296656. eCollection 2024.
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.
科学技术的不断发展，使得校园建设逐渐走向数字化和智能化。为了将人脸识别技术应用于高等教育中的智能图书馆建设，本研究对传统的人脸识别算法模型进行了优化。首先，设计了以人脸识别为核心的高校图书馆智能管理系统，其次，对传统的 FaceNet 网络进行了优化。结合 MobileNet、注意力机制、感受野模块和 Mish 激活函数，构建了改进的多任务人脸识别卷积神经网络，并应用于高校智能图书馆建设中。所构建模型的性能验证表明，模型在稳定状态下的特征匹配误差值仅为 0.04。在数据集上的识别准确率高达 99.05%，识别误差低至 0.51%。用于高校智能图书馆的人脸识别模型可达到 97.6%的教师满意度和 96.8%的学生满意度。综上所述，本文构建的人脸识别模型具有良好的识别性能，可以为智能图书馆建设提供有效的技术支持。
