دانلود مقاله انگلیسی رایگان:GaitCode: احراز هویت پیوسته مبتنی بر راه رفتن با استفاده از یادگیری چند حالته و حسگرهای پوشیدنی - 2021
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  • GaitCode: Gait-based continuous authentication using multimodal learning and wearable sensors GaitCode: Gait-based continuous authentication using multimodal learning and wearable sensors
    GaitCode: Gait-based continuous authentication using multimodal learning and wearable sensors

    سال انتشار:

    2021


    عنوان انگلیسی مقاله:

    GaitCode: Gait-based continuous authentication using multimodal learning and wearable sensors


    ترجمه فارسی عنوان مقاله:

    GaitCode: احراز هویت پیوسته مبتنی بر راه رفتن با استفاده از یادگیری چند حالته و حسگرهای پوشیدنی


    منبع:

    Sciencedirect - Elsevier - Smart Health, 19 (2021) 100162: doi:10:1016/j:smhl:2020:100162


    نویسنده:

    Ioannis Papavasileiou


    چکیده انگلیسی:

    The ever-growing threats of security and privacy loss from unauthorized access to mobile devices have led to the development of various biometric authentication methods for easier and safer data access. Gait-based authentication is a popular biometric authentication as it utilizes the unique patterns of human locomotion and it requires little cooperation from the user. Existing gait-based biometric authentication methods however suffer from degraded performance when using mobile devices such as smart phones as the sensing device, due to multiple reasons, such as increased accelerometer noise, sensor orientation and positioning, and noise from body movements not related to gait. To address these drawbacks, some researchers have adopted methods that fuse information from multiple accelerometer sensors mounted on the human body at different lo- cations. In this work we present a novel gait-based continuous authentication method by applying multimodal learning on jointly recorded accelerometer and ground contact force data from smart wearable devices. Gait cycles are extracted as a basic authentication element, that can continuously authenticate a user. We use a network of auto-encoders with early or late sensor fusion for feature extraction and SVM and soft max for classification. The effectiveness of the proposed approach has been demonstrated through extensive experiments on datasets collected from two case studies, one with commercial off-the-shelf smart socks and the other with a medical-grade research prototype of smart shoes. The evaluation shows that the proposed approach can achieve a very low Equal Error Rate of 0.01% and 0.16% for identification with smart socks and smart shoes respectively, and a False Acceptance Rate of 0.54%–1.96% for leave-one-out authentication.
    Keywords: Biometric authentication | Gait authentication | Autoencoders | Sensor fusion | Multimodal learning | Wearable sensors


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 18
    حجم فایل: 4800 کیلوبایت

    قیمت: رایگان


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