基于级联卷积神经网络的人脸特征点识别算法实现

针对在有冗余图像信息干扰下进行人脸有效特征点提取时精度不高的问题,提出了基于级联卷积神经网络的人脸特征点检测算法.在该算法中:输入层读入规则化的原始图像,神经元提取图像的局部特征;池化层进行局部平均和降采样操作,对卷积结果降低维度;卷积层和池化层分布连接,迭代训练,输出特征点检测结果.该算法采用Python语言编程实现,在人脸数据集进行仿真实验,结果表明该算法对人脸特征点有较高的识别率.

Realization of face feature point recognition based on cascaded convolutional neural network

If a face image is interfered by redundant information, the accuracy of the extracted effective feature points from the image is not high enough. To solve this problem, a face feature point detection algorithm based on a cascade convolutional neural network is proposed in this paper. The algorithm reads in information of an original image via the input-function first, then extracts local features of the image through neurons in a receptive domain, and inputs all local features into a pooling domain. After doing that, the algorithm is able to average all captured local features stored in the pooling domain, and further do down-sampling to the pooling domain, and reduce the dimension of the convolution results, and finally output detection results of those feature points by virtual of iterative training. In this study, Python language is used to program the algorithm, and simulation experiments are carried out with the help of face data set. Simulation results show that the algorithm has a high recognition rate for face feature points.