ارائه یک روش ترتیبی پویا بر اساس یادگیری عمیق به منظور بهبود کارایی سیستم‌های تطبیق بیومتریکی مبتنی بر کارت هوشمند
محورهای موضوعی : مهندسی برق و کامپیوتر
محمد صبری
1
,
محمد شهرام معین
2
,
فربد رزازی
3
1 - دانشگاه آزاد اسلامی واحد علوم و تحقیقات
2 - مرکز تحقیقات مخابرات ایران
3 - دانشگاه آزاد واحد علوم و تحقیقات
کلید واژه: تصدیق هویتیادگیری عمیقتطبیق در بستر کارتچند بیومتریکی,
چکیده مقاله :
امروزه با افزایش تهدیداتی نظیر تروریسم و جرایم سایبری، فرایند تصدیق هویت افراد اهمیت چشم‌گیری یافته و متضمن امنیت ملی یک کشور تلقی می‌گردد. در این پژوهش، یک روش ترتیبی بر اساس یادگیری عمیق جهت مدیریت پویای جریان الگوریتم سیستم‌های تصدیق هویت چند بیومتریکی ارائه شده است. روش پیشنهادی دارای این مزیت است که معیارهای ویژگی به صورت ضمنی و اتوماتیک توسط یک شبکه عمیق با معماری انتها به انتها استخراج می‌گردند. یک سیستم تصدیق هویت چند بیومتریکی شامل دو انگشت و چهره مبتنی بر روش پیشنهادی نیز پیاده‌سازی گردیده است. بر طبق نتایج، در مجموع تصدیق هویت برای 42/91% موارد بر اساس اثر انگشت انجام شده و فقط برای 58/8% موارد نیاز به استفاده از مشخصه چهره بوده است. این در حالی است که روش پیشنهادی نسبت به انگشت اول و دوم به ترتیب 35 و 30% دقت بالاتری نیز داشته است. دستاوردهای این پژوهش می‌تواند نقش مهمی در مقبولیت و موفقیت پروژه‌های عملیاتی و میزان اثربخشی آنها در فرایند تصدیق هویت داشته باشد زیرا از یک طرف دارای دقت بیشتری بوده و از طرف دیگر منجر به کاهش هزینه یعنی زمان مورد نیاز برای فرایند اخذ و تطبیق گردیده که باعث می‌شود هم‌زمان رضایتمندی خدمتگیرنده و امنیت خدمتدهنده فراهم آید.
Nowadays, the threats such as terrorism and cybercrime are extremely increased, therefore, the identity authentication process is very substantial for the national security of a country. In this paper, we propose a novel multimodal authentication system with sequential structure based on deep learning. In the proposed method, feature vectors are extracted automatically through deep network with an end to end architecture. A multi biometric system using two fingerprint and a face is implemented and evaluated. The results demonstrate that, the authentication is done by fingerprints in 91.42% cases and only for 8.58% cases the face modal is required. In addition, the proposed method is more accurate than first and second fingerprint by 35% and 30% at FMR=0.001, respectively. As a result, we augmented the accuracy of the system and at the same time reduced the acquisition and matching time. This conducts to the improvement of user convenience and security of the service provider, simultaneously. The achievements of this work can be used to increase the effectiveness of authentication process and can play an important role in the acceptability of real world applications.
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