基于卷积神经网络的人脸检测算法研究

近年来，随着深度学习的迅猛发展，人脸检测算法准确度已有很大提升。模型越复杂，检测速度越慢，设计一种准确度与速度兼顾的人脸检测模型尤为必要。基于FaceBoxes人脸检测算法框架，提出一种基于深层卷积主干网络的改进方法，并在人脸检测基准数据集中进行测试实验。其在FDDB数据集上的实验结果显示，检测正确率达95%，比传统方法提高1.67%。该算法在保证实时性的同时提升了检测准确率，可应用于追求更高准确率的人脸检测系统。

Research on Face Detection Algorithm Based on Convolutional Neural Network

Thanks to the rapid development of deep learning in recent years， the accuracy of face detection algorithm has been greatly improved compared with the earlier algorithms. However， the more complex the model detection speed will be slower， so the design of a face detection model with both accuracy and speed has become a major topic in this field. Based on FaceBoxes， a face detection algorithm framework， this paper proposes an improved method of deep convolutional backbone network， and conducts test experiments in the face detection benchmark data set. The experimental results on the FDDB data set showed that the detection accuracy reached 95%， which was 1.67% higher than the traditional method. The algorithm in this paper not only guarantees real-time performance but also improves the accuracy of detection， which can be used in more accurate face detection systems.