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Evaluation of Features Selection in Enhancing the Performance of Palm Print Recognition

تقويم فعالية اختيار السمات الأفضل في تحسين التعرف على الأشخاص باستخدام صورة راحة اليد

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




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This paper introduces a new approach to extract palm print features and select the best ones. The paper also studies the effectiveness of the selection process on speed and performance of system.


Artificial intelligence review:
Research summary
تقدم الدراسة طريقة جديدة لاستخلاص سمات صورة راحة اليد وتحديد السمات الأكثر فعالية منها، إضافة إلى دراسة تأثير عملية الاختيار على تسريع عمل مراحل النظام ورفع أدائه. يقترح البحث الحالي إنجاز أربع مراحل أساسية تتضمن الأولى عملية معالجة مسبقة لصور البد لتصحيح دورانها واتجاهها، أما المرحلة الثانية فتشمل تجزيء صورة اليد واستخلاص نقاط التلاقى بين الأصابع والحصول على منطقة الأصابع وخطوط الحياة، أما المرحلة الثالثة فتمثل عملية استخلاص السمات وهى الزوايا المنتشكلة من نقاط التلاقى بين الأصابع وسماكة الأصابع والسمات الإحصائية لخطوط الحياة، وفى المرحلة الأخيرة تم إنجاز عملية التعرف والتصنيف. طبقت مرحلة التجزيء على قاعدة بيانات CASIA لصور اليد المؤلفة من (1200) صورة تعود لـ (300) شخص وتم التوصل لدقة تجزيء 97.8%. تم أخذ 160 صورة مجزأة تعود لـ 40 شخص واستخدمت كقاعدة بيانات لمرحلة التعرف، و 290 صورة اختبار وتم التوصل لمعدل تعرف 96.66%.
Critical review
دراسة نقدية: تقدم هذه الدراسة إسهاماً مهماً في مجال التعرف على الأشخاص باستخدام صور راحة اليد، حيث تم التركيز على تحسين دقة وسرعة النظام من خلال اختيار السمات الأكثر فعالية. ومع ذلك، يمكن الإشارة إلى بعض النقاط التي قد تحتاج إلى مزيد من التحليل والتطوير. أولاً، لم يتم التطرق بشكل كافٍ إلى تأثير التغيرات البيئية مثل الإضاءة والاتجاه على دقة النظام، وهو ما يمكن أن يكون له تأثير كبير في التطبيقات العملية. ثانياً، قد يكون من المفيد مقارنة نتائج هذه الدراسة مع نتائج دراسات أخرى استخدمت تقنيات مختلفة لاستخلاص السمات، وذلك لتحديد مدى تفوق الطريقة المقترحة. أخيراً، يمكن أن يكون هناك اهتمام أكبر بتحليل الزمن اللازم لكل مرحلة من مراحل النظام بشكل أكثر تفصيلاً، وذلك لتحديد النقاط التي يمكن تحسينها بشكل أكبر.
Questions related to the research
  1. ما هي المراحل الأساسية الأربع التي يقترحها البحث لاستخلاص سمات صورة راحة اليد؟

    تشمل المراحل الأربع: المعالجة المسبقة لتصحيح دوران واتجاه صورة اليد، تجزيء صورة اليد واستخلاص نقاط التلاقى بين الأصابع، استخلاص السمات مثل الزوايا وسماكة الأصابع والسمات الإحصائية لخطوط الحياة، وأخيراً مرحلة التعرف والتصنيف.

  2. ما هي قاعدة البيانات المستخدمة في الدراسة وما هي دقة التجزيء التي تم التوصل إليها؟

    تم استخدام قاعدة بيانات CASIA لصور اليد المؤلفة من 1200 صورة تعود لـ 300 شخص، وتم التوصل لدقة تجزيء 97.8%.

  3. ما هي السمات التي تم استخلاصها في المرحلة الثالثة من البحث؟

    تم استخلاص الزوايا المتشكلة من نقاط التلاقى بين الأصابع، سماكة الأصابع، والسمات الإحصائية لخطوط الحياة.

  4. ما هو معدل التعرف الذي تم التوصل إليه في مرحلة التعرف والتصنيف؟

    تم التوصل إلى معدل تعرف 96.66%.


References used
(Doublet J., Lepetit O. and Revenu M., 2006 "Contact-less Hand Recognition using shape and texture features", 8th International Conference on Signal Processing, Vol (3
Funada. J., Ohta N., Mizoguchi M., Temma T., Nakanishi K., Murai A., Sugiuchi T., Wakabayashi T., and Yamada Y 1998 “Feature extraction method for palmprint considering elimination of creases,” Proc.14th International Conference of Pattern Recognition, vol(2), pp. 1849 -1854
Han C. -C., Cheng H.-L., Lin C.-L. and Fan K.-C., 2003 "Personal authentication using palm print features," Pattern Recognition Journal, vol (36), pp. 371-381
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