• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Neural computing & applications >Fingerprint pattern identification and classification approach based on convolutional neural networks
【24h】

Fingerprint pattern identification and classification approach based on convolutional neural networks

机译：基于卷积神经网络的指纹图案识别与分类方法

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Fingerprint pattern recognition and classification can be of assistance in the research on human personality. In some previous studies, fingerprints were classified into four categories to speed up recognition, but the method of that classification is not suitable for researching the diversity of human personalities. Therefore, in this paper, fingerprint patterns were classified into six types and the accuracy of the recognition was improved to facilitate the research on human personality characteristics. Based on this idea, a six-category fingerprint database is annotated manually and a convolutional neural network (CNN) is proposed for identifying real fingerprint patterns. The new CNN consists of four convolutional layers, three max-pooling layers, two norm layers, and three fully connected layers. The best accuracy the model achieved was 94.87% for a six-category fingerprint database and 92.9% accuracy for a four-category fingerprint database. The results of experimental tests show that the proposed model can recognize the pattern features from a large fingerprint database using the automatic learning and feature extraction abilities of the CNN to get a greater accuracy than in previous experiments.
机译：指纹模式识别和分类可以在人性化研究中有所帮助。在某些先前的研究中，指纹被分为四个类别以加速识别，但该分类的方法不适合研究人类人物的多样性。因此，在本文中，指纹图案被分为六种类型，提高了识别的准确性，以促进人类特征的研究。基于该思想，提出了一种手动注释六类指纹数据库，并提出卷积神经网络（CNN）来识别真正的指纹图案。新的CNN由四个卷积层，三个最大池层，两个规范层和三个完全连接的层组成。对于六类指纹数据库实现的型号的最佳精度为94.87％，为四类指纹数据库的精度为94.87％。实验测试的结果表明，所提出的模型可以使用CNN的自动学习和特征提取能力来识别来自大指纹数据库的模式特征，以获得比先前实验更高的精度。

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