C-Canny算法和改进单层神经网络相结合的面部特征点定位  

Facial feature point localization based on C-Canny algorithm and improved single neural network

作　　者：付文博 何欣[1,2] 于俊洋 FU Wen-bo;HE Xin;YU Jun-yang(School of Software,Henan University,Kaifeng 475000;Henan Intelligent Data Processing Engineering Research Center,Kaifeng 475000,China)

机构地区：[1]河南大学软件学院,河南开封475000 [2]河南省智能数据处理工程研究中心,河南开封475000

出　　处：《计算机工程与科学》2020年第4期658-664,共7页Computer Engineering & Science

基　　金：国家自然科学基金(61602525);河南省科技发展计划项目(大数据环境下的算法研究)(182102210229)。

摘　　要：深度学习在面部特征点识别领域已取得了较为显著的成果,然而在处理遮挡、光照、角度不当等复杂条件下的面部图像时,预测数目较多的面部特征点仍是一个具有挑战性的问题。为解决面部多特征点在复杂条件下的定位问题,设计了一种C-Canny算法和改进单层神经网络相结合的网络结构,将传统Canny算法应用到面部区域定位阶段,使得神经网络可以快速进行面部区域重定位,从而提升识别的准确率。实验结果表明,在300-w和300-vw数据集上与一些传统方法、神经网络相比,该神经网络结构将损失函数的值平均降低了12.2%。Deep learning has achieved remarkable results in the field of facial recognition. However, when dealing with facial images under complex conditions such as occlusion, illumination and improper angles, predicting a large number of facial feature points is still a challenging problem. In order to solve the localization problem of multiple facial feature points under complex conditions, this paper designs a network structure based on C-Canny algorithm and improved single neural network. The traditional Canny algorithm is applied to the face region localization stage, so that the neural network can quickly reposition the face region to improve the accuracy of model recognition. Experimental results show that, compared with some existing traditional algorithms and neural networks, the neural network structure reduces the value of loss function by 12.2% on average on the 300-w and 300-vw datasets.

关 键 词：深度学习 卷积神经网络 面部特征提取 区域再定位 

分 类 号：TP399[自动化与计算机技术—计算机应用技术]