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Using Multi-Sets of Features to improve the Performance of Automatic Signature Verification Systems

استخدام مجموعات الخصائص المتعددة لرفع أداء أنظمة التحقق من صحة التواقيع

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 Publication date 2010
and research's language is العربية
 Created by Shamra Editor




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For decades, published Automatic Signature Verification (ASV) works depended on using one feature set. Some researchers selected this feature set based on their experience, and some others selected it using some feature selection algorithms that can select the best feature set (bfs). In practical systems, the documents containing the signatures could be noisy, and recognition of check writer in multi-signatory accounts is required. Due to the error caused by such requirements and data quality, improving the performance of ASV becomes a necessity. In this paper, a new technique for ASV decision making using Multi-Sets of Features is introduced. The experimental results have shown that the introduced technique gives important improvement in forgery detection and in the overall performance of the system.


Artificial intelligence review:
Research summary
تتناول هذه الورقة البحثية تحسين أداء أنظمة التحقق التلقائي من التوقيعات (ASV) باستخدام مجموعات متعددة من الميزات. تقليديًا، اعتمدت الأعمال المنشورة في هذا المجال على مجموعة واحدة من الميزات، ولكن هذه الورقة تقترح تقنية جديدة تعتمد على استخدام مجموعات متعددة من الميزات لتحسين دقة الكشف عن التزوير. تم اختبار التقنية الجديدة باستخدام بيانات توقيعات متنوعة، وأظهرت النتائج التجريبية تحسنًا ملحوظًا في أداء النظام، خاصة في الكشف عن التزوير المهاري. تتضمن الورقة شرحًا مفصلًا للبيانات المستخدمة، وتقنيات استخراج الميزات، وطرق قياس المسافة، وآلية اتخاذ القرار في التحقق من التوقيعات. كما تقدم الورقة نتائج تجريبية تُظهر فعالية التقنية الجديدة مقارنة بالطرق التقليدية.
Critical review
دراسة نقدية: تعتبر هذه الورقة إضافة مهمة إلى مجال التحقق التلقائي من التوقيعات، حيث تقدم نهجًا مبتكرًا يعتمد على استخدام مجموعات متعددة من الميزات لتحسين دقة النظام. ومع ذلك، يمكن توجيه بعض النقد إلى الدراسة من حيث تعقيد التقنية المقترحة، مما قد يجعل من الصعب تنفيذها في الأنظمة العملية. بالإضافة إلى ذلك، قد يكون من المفيد تقديم مقارنة أكثر تفصيلًا بين الأداء الفعلي للنظام المقترح والأنظمة التقليدية في بيئات مختلفة. كما أن الدراسة قد تستفيد من تحليل أعمق لتأثير الضوضاء على أداء النظام المقترح.
Questions related to the research
  1. ما هي التقنية الجديدة التي تم اقتراحها في الورقة لتحسين أداء أنظمة التحقق التلقائي من التوقيعات؟

    التقنية الجديدة تعتمد على استخدام مجموعات متعددة من الميزات لتحسين دقة الكشف عن التزوير في أنظمة التحقق التلقائي من التوقيعات.

  2. ما هي أنواع الميزات التي يتم استخراجها في أنظمة التحقق التلقائي من التوقيعات؟

    يتم استخراج نوعين من الميزات: الميزات الشكلية مثل ميل الكتابة والميزات الزائفة الديناميكية مثل عامل الضغط العالي.

  3. كيف يتم قياس المسافة بين التوقيع المدخل والتوقيعات المرجعية في النظام؟

    يتم قياس المسافة باستخدام مسافة إقليدية، حيث يتم حسابها من ميزات التوقيع التجريبي باستخدام معادلة محددة.

  4. ما هي الفائدة الرئيسية لاستخدام مجموعات متعددة من الميزات في التحقق من التوقيعات؟

    الفائدة الرئيسية هي تحسين دقة الكشف عن التزوير من خلال جمع الفعالية التي يمكن أن توفرها مجموعات الميزات الفعالة التي لا يمكن التقاطها باستخدام مجموعة ميزات واحدة فقط.


References used
M. Ammar, Y. Yoshida and T. Fukumura, Automatic Off-line Verification of Signatures Based on Pressure Features", IEEE, Trans on Systems Man and Cybernetics, Vol. SMC-16, No 3, pp 39-47, 1986
M. Ammar, et al., A New Effective Approach for Automatic Off-line Verification of Signatures by Using Pressure Features, Proceedings of the 8th Int. Conf. on Pattern Recognition, Paris, pp. 566-569, Oct. 1986
K. Huang and Y. Hong, Off-line signature verification based on geometric feature extraction and neural network classification, Patten Recognition, Vol. 30, No. 1, pp. 9-17, 1997
C. Sansone and M. Vento, Signature verification: increasing performance by a multi-stage system, Pattern Analysis and Applications, Vol. 3, pp. 169-181,2000
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