School of Urban Geology and Engineering of Hebei University of Geosciences, Shijiazhuang 050031, Hebei, China.
Comput Intell Neurosci. 2022 Aug 16;2022:3961910. doi: 10.1155/2022/3961910. eCollection 2022.
With the increase in the number of data, the traditional shallow image features cannot meet the needs of image representation. As an important means of image research, deep learning network has been paid attention to. In the field of face image evaluation, deep learning algorithm has been introduced, and the recognition technology has gradually matured. Based on this, this paper studies the application of face image evaluation algorithm of deep learning mobile terminal for student check-in management. A face image detection model for student check-in management is constructed, and a deep learning network is used to realize face detection. A face detection algorithm based on candidate region joint deep learning network is designed, and a face key point detection method based on cascaded convolution network is proposed. Aiming at the low efficiency of face recognition and detection, the existing loss function is optimized, the extraction algorithm of face binary features is proposed, and experiments are designed to analyze the performance of the algorithm. The simulation results show that the face detection based on the improved deep learning network can shorten the retrieval time and improve the accuracy of face image classification.
随着数据数量的增加，传统的浅层图像特征已经不能满足图像表示的需求。深度学习网络作为图像研究的重要手段，已经受到了关注。在人脸图像评价领域，引入了深度学习算法，识别技术逐渐成熟。在此基础上，本文研究了深度学习移动端人脸图像评价算法在学生签到管理中的应用。构建了用于学生签到管理的人脸图像检测模型，利用深度学习网络实现人脸检测。设计了一种基于候选区域联合深度学习网络的人脸检测算法，提出了一种基于级联卷积网络的人脸关键点检测方法。针对人脸识别和检测效率低的问题，对现有的损失函数进行优化，提出了人脸二进制特征的提取算法，并设计实验对算法性能进行分析。仿真结果表明，基于改进深度学习网络的人脸检测可以缩短检索时间，提高人脸图像分类的准确率。
