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Personal Identification via Handprint with use of Zero-Crossing Technology

التعرف على الأشخاص عن طريق راحة اليد باستخدام تقنية التقاطعات الصفرية

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 Publication date 2016
and research's language is العربية
 Created by Shamra Editor




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Personal identification based on handprint has been gaining more attention with the increasing needs of high level of security. In this study a novel approach for human recognition based on handprint is proposed. Wavelet transform was used to extract features presented in the palm image based on wavelet zero-crossing method. Firstly the wavelet transform of the whole palm image at the fourth level was worked out, which results in four matrices; three of them are detail matrices (i.e., horizontal, vertical and diagonal) as well as one approximation matrix. Throughout this study, only the detail matrices were used because the required information (i.e., hand lines and curves) is included in those matrices. Sixteen features were extracted from each detail matrix, and then arranged in one vector. Consequently, for each palm sample a feature vector consisting of 48 input features of the used neural network was obtained. For this purpose, a database consisting of 400 palm images belonging to 40 people at the rate of 10 images per person was built. Practical tests outcome showed that the designed system successfully indentified 91.36% of the tested images.


Artificial intelligence review:
Research summary
تتناول هذه الدراسة تقنية جديدة للتعرف على الأشخاص باستخدام بصمة راحة اليد، حيث تعتمد على استخراج السمات من معاملات التحويل المويجي لصور راحة اليد باستخدام تقنية التقاطعات الصغرية. يتم استخراج السمات من مصفوفات التفاصيل الأفقية والشاقولية والقطرية، وتكوين شعاع سمات مكون من 48 سمة لكل عينة يد. تم بناء قاعدة بيانات مكونة من 400 صورة لراحة اليد تعود لأربعين شخصًا، بمعدل 10 صور لكل شخص. أظهرت الاختبارات العملية أن النظام المصمم نجح في التعرف على الأشخاص بمعدل 91.36% في حالة العينات القياسية و83.41% في حالة العينات المشوهة. تقدم الدراسة مقارنة مع تقنيات أخرى وتوصي بتطوير النظام من خلال تقليل عدد السمات المستخلصة ودمج تقنيات بيومترية أخرى لتحسين نسبة التعرف.
Critical review
تعد هذه الدراسة خطوة مهمة في مجال التعرف على الأشخاص باستخدام بصمة راحة اليد، حيث تقدم تقنية جديدة تعتمد على التحويل المويجي وتقنية التقاطعات الصغرية. ومع ذلك، يمكن تحسين الدراسة من خلال توسيع قاعدة البيانات لتشمل عدد أكبر من الأشخاص وزيادة نسبة التشوه في العينات لاختبار فعالية النظام في ظروف أكثر تعقيدًا. كما يمكن تحسين الأداء من خلال دمج تقنيات بيومترية أخرى مثل بصمة الإصبع وهندسة اليد. بالإضافة إلى ذلك، يجب تقليل عدد العينات المطلوبة لتدريب الشبكة العصبونية لتحقيق نظام أسرع وأكثر فعالية.
Questions related to the research
  1. ما هي التقنية الجديدة المقترحة في هذه الدراسة للتعرف على الأشخاص؟

    التقنية الجديدة المقترحة تعتمد على استخراج السمات من معاملات التحويل المويجي لصور راحة اليد باستخدام تقنية التقاطعات الصغرية.

  2. ما هي نسبة نجاح النظام في التعرف على الأشخاص باستخدام العينات القياسية؟

    نجح النظام في التعرف على الأشخاص بنسبة 91.36% باستخدام العينات القياسية.

  3. ما هي التوصيات التي قدمتها الدراسة لتطوير النظام؟

    توصي الدراسة بتقليل عدد السمات المستخلصة، دمج تقنيات بيومترية أخرى، تقليل عدد العينات المطلوبة لتدريب الشبكة العصبونية، وتوسيع قاعدة البيانات.

  4. ما هي الفروقات بين التحويل المويجي وتحويل فورييه كما هو موضح في الدراسة؟

    التحويل المويجي يتيح تحليل الإشارة في الزمن والتردد معًا، بينما تحويل فورييه يحلل الإشارة في التردد فقط، مما يجعل التحويل المويجي أكثر فعالية في استخراج السمات من الصور.


References used
KAUR, G., SINGH, G. AND KUMAR, V. A Review on Biometric Recognition. International Journal of Bio-Science and Bio-Technology, 6(2014), 69-76
ZHANG, D., KONG, W.K., YOU,J. and WONG, M. Biometrics online palmprint identification. IEEE Transactions on Pattern Analysis and Machine Intelligence 25 (2003), 1041-1050
WOODARD, J. D., ORLANS, N. M., and HIGGINS, P. T." Biometric:Identity Assurance in the Information Age", McGraw-Hill, New York, 2003. pp: 45-115
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