首页> 外文期刊>International journal of information system modeling and design >Multimodal Biometric Based on Fusion of Ridge Features with Minutiae Features and Face Features
【24h】

Multimodal Biometric Based on Fusion of Ridge Features with Minutiae Features and Face Features

机译:基于脊特征与细节特征和面部特征融合的多峰生物识别

获取原文
获取原文并翻译 | 示例
           

摘要

Multimodal biometrics refers to the exploiting combination of two or more biometric modalities in an identification of a system. Fingerprint, face, retina, iris, hand geometry, DNA, and palm print are physiological traits while voice, signature, keystrokes, gait are behavioural traits used for identification by a system. Single biometric features like faces, fingerprints, irises, retinas, etc., deteriorate or change with time, environment, user mode, physiological defects, and circumstance therefore integrating multi features of biometric traits increase robustness of the system. The proposed multimodal biometrics system presents recognition based on face detection and fingerprint physiological traits. This proposed system increases the efficiency, accuracy and decreases execution time of the system as compared to the existing systems. The performance of proposed method is reported in terms of parameters such as False Rejection Rate (FRR), False Acceptance Rate (FAR) and Equal Error Rate (EER) and accuracy is reported at 95.389%.
机译:多模式生物识别技术是指在系统识别中利用两种或多种生物识别模式的组合。指纹,面部,视网膜,虹膜,手部几何形状,DNA和掌纹是生理特征,而语音,签名,击键,步态则是系统用来识别的行为特征。诸如面部,指纹,虹膜,视网膜等的单个生物特征随时间,环境,用户模式,生理缺陷和环境而恶化或变化,因此整合生物特征的多种特征可以提高系统的鲁棒性。拟议的多模式生物识别系统提出了基于面部检测和指纹生理特征的识别。与现有系统相比,该提出的系统提高了效率,准确性并减少了系统的执行时间。所报告的方法的性能以错误拒绝率(FRR),错误接受率(FAR)和均等错误率(EER)等参数表示,准确度为95.389%。

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