基于卷积神经网络的非限定性条件下的人脸识别研究
发布时间：2018-07-25 12:45
【摘要】：卷积神经网络是将人工神经网络技术与深度学习方法相结合的一种新型人工神经网络模型,因为在大型图像处理中具有出色的表现,使得其在计算机视觉领域得到了广泛的应用。非限定性条件下的人脸识别难度较大,应用传统的人脸识别方法难以获得令人满意的结果。通过设计一个深度卷积神经网络,对人脸样本进行特征学习,可以使在非限定条件下的人脸识别具有较高的准确率。本文的目的在于设计一种具有较高鲁棒性的深度卷积神经网络,用于非限定性条件下的人脸识别,在保证准确率的前提下提高系统的运算效率。本文的主要内容包括:首先,给出了卷积神经网络的理论推导。对在手写字符识别领域应用广泛的LeNet-5网络模型结构进行了说明,然后介绍了一种改进的LeNet-5网络用于普通的人脸识别。通过实验说明了改进后的LeNet-5可以在普通的人脸识别方面取得较好的成果。其次,对在非限定性人脸识别领域取得较好测试效果的VGG-19网络的结构特点与参数配置进行了分析,提出了一种针对VGG-19网络模型的改进方法。通过对原始的VGG-19网络结构进行修改,合理减少网络训练参数,在保证一定准确率的情况下,降低了原网络较为苛刻的硬件要求,提高了网络的运算效率。然后,在人脸数据库FaceScrub上对新设计的卷积神经网络模型进行了训练与测试,取得了较好的识别效果。最后,对实验结果进行了分析,指出了该网络的优点及缺点。
[Abstract]:Convolution neural network is a new artificial neural network model which combines artificial neural network technology and deep learning method. Because it has excellent performance in the large image processing, it has been widely used in the field of computer vision. The face recognition under non restrictive condition is difficult, and the traditional face is applied. The recognition method is difficult to obtain a satisfactory result. By designing a deep convolution neural network, the feature learning of the face samples can make the face recognition with non finite conditions have a higher accuracy. The purpose of this paper is to design a kind of deep convolution neural network with high robustness for non restrictive conditions. The main contents of this paper are as follows: first, the theoretical derivation of the convolution neural network is given. A wide application of LeNet-5 network model structure in the field of handwritten character recognition is described, and then an improved LeNet-5 network is introduced for common use. Face recognition. The experimental results show that the improved LeNet-5 can achieve good results in common face recognition. Secondly, the structural features and parameter configuration of VGG-19 network with better test results in the field of non restrictive face recognition are analyzed, and an improved method for VGG-19 network model is proposed. To modify the original VGG-19 network structure, reduce the network training parameters reasonably, reduce the hard hardware requirements of the original network and improve the computing efficiency of the network. Then, the new design of the convolution neural network model is trained and tested on the face database FaceScrub. Finally, the experimental results are analyzed, and the advantages and disadvantages of the network are pointed out.
【学位授予单位】：哈尔滨工业大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP391.41;TP183
本文编号：2143871
[Abstract]:Convolution neural network is a new artificial neural network model which combines artificial neural network technology and deep learning method. Because it has excellent performance in the large image processing, it has been widely used in the field of computer vision. The face recognition under non restrictive condition is difficult, and the traditional face is applied. The recognition method is difficult to obtain a satisfactory result. By designing a deep convolution neural network, the feature learning of the face samples can make the face recognition with non finite conditions have a higher accuracy. The purpose of this paper is to design a kind of deep convolution neural network with high robustness for non restrictive conditions. The main contents of this paper are as follows: first, the theoretical derivation of the convolution neural network is given. A wide application of LeNet-5 network model structure in the field of handwritten character recognition is described, and then an improved LeNet-5 network is introduced for common use. Face recognition. The experimental results show that the improved LeNet-5 can achieve good results in common face recognition. Secondly, the structural features and parameter configuration of VGG-19 network with better test results in the field of non restrictive face recognition are analyzed, and an improved method for VGG-19 network model is proposed. To modify the original VGG-19 network structure, reduce the network training parameters reasonably, reduce the hard hardware requirements of the original network and improve the computing efficiency of the network. Then, the new design of the convolution neural network model is trained and tested on the face database FaceScrub. Finally, the experimental results are analyzed, and the advantages and disadvantages of the network are pointed out.
【学位授予单位】：哈尔滨工业大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP391.41;TP183
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，本文编号：2143871
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