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Register a new userUsing 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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معن عمار( باحث )
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لعقود من الزمن، اعتمدت البحوث المنشورة في مجال التحقق الآلي من صحة التواقيع على استخدام مجموعة خصائص واحدة. اختار بعض الباحثين مجموعة الخصائص هذه اعتماداً على خبرتهم في هذا المجال، و اختارها آخرون اعتماداً على خوارزميات انتقاء الخصائص التي تستطيع اختيار أفضل مجموعة خصائص. في الأنظمة العملية، يمكن أن تحتوي وثائق التواقيع ضجيجاً، و يمكن أن يكون تعرف كاتب الشيك مطلوباً في الحسابات التي يوقع شيكاتها أكثر من شخص. و نظراً إلى انخفاض مستوى أداء النظام الناتج عن مثل هذه المتطلبات، يصبح تحسين أداء نظام التحقق من صحة التواقيع ضرورة. تعرض ورقة البحث هذه تقنية جديدة لاتخاذ القرار بالاعتماد على عدة مجموعات خصائص بدلاً من واحدة فقط. و قد أظهرت النتائج التجريبية أن التقنية الجديدة تعطي تحسيناً مهماً في القدرة على كشف التزوير، و في الأداء العام للنظام.
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.
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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statistical functions
التعرف على النماذج
صورة التوقيع
كشف التوقيع
سمات من صورة التوقيع
التحقق من صحة الأشخاص باستخدام صورة التوقيع
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