دانلود مقاله انگلیسی رایگان:همجوشی امتیاز موازی ECG و اثر انگشت را برای احراز هویت انسان بر اساس شبکه های عصبی کانولوشن - 2019
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دانلود مقاله انگلیسی شبکه های عصبی رایگان
  • Parallel score fusion of ECG and fingerprint for human authentication based on convolution neural network Parallel score fusion of ECG and fingerprint for human authentication based on convolution neural network
    Parallel score fusion of ECG and fingerprint for human authentication based on convolution neural network

    سال انتشار:

    2019


    عنوان انگلیسی مقاله:

    Parallel score fusion of ECG and fingerprint for human authentication based on convolution neural network


    ترجمه فارسی عنوان مقاله:

    همجوشی امتیاز موازی ECG و اثر انگشت را برای احراز هویت انسان بر اساس شبکه های عصبی کانولوشن


    منبع:

    Sciencedirect - Elsevier - Computers & Security, 81 (2019) 107-122: doi:10:1016/j:cose:2018:11:003


    نویسنده:

    Mohamed Hammad a , b , Kuanquan Wang


    چکیده انگلیسی:

    Biometrics have been extensively used in the past decades in various security systems and have been deployed around the world. However, all unimodal biometrics have their own limitations and disadvantages (e.g., fingerprint suffers from spoof attacks). Most of these limitations can be addressed by designing a multimodal biometric system, which deploys over one biometric modality to improve the performance and make the system robust to spoof attacks. In this paper, we proposed a secure multimodal biometric system by fusing electrocardiogram (ECG) and fingerprint based on convolution neural network (CNN). To the best of our knowledge, this is the first study to fuse ECG and fingerprint using CNN for human authentication. The feature extraction for individual modalities are performed using CNN and then biometric templates are generated from these features. After that, we have applied one of the cancelable biometric techniques to protect these templates. In the authentication stage, we proposed a Q-Gaussian multi support vector machine (QG-MSVM) as a classifier to improve the authentication performance. Dataset augmentation is successfully used to increase the authentication performance of the proposed system. Our system is tested on two databases, the PTB database from PhysioNet bank for ECG and LivDet2015 database for the fingerprint. Experimental results show that the proposed multimodal system is efficient, robust and reliable than existing multimodal authentication algorithms. According to the advantages of the proposed system, it can be deployed in real applications
    Keywords: Authentication | CNN | ECG | Fingerprint | Multimodal biometrics | MSVM


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 16
    حجم فایل: 1421 کیلوبایت

    قیمت: رایگان


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