Feature Extraction of Face Image Based on Convolutional Neural Network

Zhaofu Lin, Liu Lei
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Abstract

In order to overcome the problem of inaccuracy in feature extraction when the corner features are not obvious in traditional corner detection methods, a face feature extraction method based on deep learning convolutional neural network is proposed in this paper. The key point of this method is the construction algorithm of convolution network with single image as input data. In this paper, the convolutional neural network implemented by Python code includes three convolution layers, three lower sampling layers and one full connection layer. The filter structure is designed and the characteristic image is obtained. The convolution layer uses the Reul function as the activation function, while the lower sampling layer uses the maximum pooling and ruul activation functions. The experimental results show that the method based on the different levels of image features extracted by deep learning network as the basis for image matching, and can still achieve good recognition effect when the traditional features such as corners are not clear.
基于卷积神经网络的人脸图像特征提取
为了克服传统角点检测方法中角点特征不明显时特征提取不准确的问题,本文提出了一种基于深度学习卷积神经网络的人脸特征提取方法。该方法的关键是单幅图像作为输入数据的卷积网络的构建算法。本文用Python代码实现的卷积神经网络包括三个卷积层、三个下采样层和一个全连接层。设计了滤波器结构,得到了特征图像。卷积层使用正则函数作为激活函数,下层采样层使用最大池化和规则激活函数。实验结果表明,该方法基于深度学习网络提取的不同层次的图像特征作为图像匹配的基础,在角点等传统特征不清晰的情况下仍能取得较好的识别效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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