• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>International conference on graphic and image processing >Face Recognition Via Gabor and Convolutional Neural Network
【24h】

Face Recognition Via Gabor and Convolutional Neural Network

机译：通过Gabor和卷积神经网络进行人脸识别

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

In recent years, the powerful feature learning and classification ability of convolutional neural network have attracted widely attention. Compared with the deep learning, the traditional machine learning algorithm has a good explanatory which deep learning does not have. Thus, In this paper, we propose a method to extract the feature of the traditional algorithm as the input of convolution neural network. In order to reduce the complexity of the network, the kernel function of Gabor wavelet is used to extract the feature from different position, frequency and direction of target image. It is sensitive to edge of image which can provide good direction and scale selection. The extraction of the image from eight directions on a scale are as the input of network that we proposed. The network have the advantage of weight sharing and local connection and texture feature of the input image can reduce the influence of facial expression, gesture and illumination. At the same time, we introduced a layer which combined the results of the pooling and convolution can extract deeper features. The training network used the open source caffe framework which is beneficial to feature extraction. The experiment results of the proposed method proved that the network structure effectively overcame the barrier of illumination and had a good robustness as well as more accurate and rapid than the traditional alorithm.
机译：近年来，卷积神经网络强大的特征学习和分类能力引起了广泛的关注。与深度学习相比，传统的机器学习算法具有很好的解释，而深度学习则没有。因此，本文提出了一种提取传统算法特征作为卷积神经网络输入的方法。为了降低网络的复杂度，利用Gabor小波的核函数从目标图像的不同位置，频率和方向提取特征。它对图像边缘敏感，可以提供良好的方向和比例选择。从比例尺的八个方向提取图像是我们提出的网络输入。该网络具有重量共享和局部连接的优点，并且输入图像的纹理特征可以减少面部表情，手势和照明的影响。同时，我们引入了一个层，该层结合了合并和卷积的结果，可以提取更深层的特征。训练网络使用了开放源代码Caffe框架，这对特征提取很有帮助。所提方法的实验结果证明，该网络结构有效克服了照明障碍，具有良好的鲁棒性，并且比传统算法具有更高的准确度和速度。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号