Earticle

초록

영어

Ensemble classifier is a combining approach to improve the accuracy of the simple classifiers. In this article, we introduced a new method for static handwritten signature verification based on an ensemble classifier. In our introduced method, after pre-processing stage, signature image is convolved with Gabor wavelets to compute the Gabor coefficients in different scales and directions. Three different feature sets are extracted from resulting Gabor coefficients using statistical approaches. A nearest neighbor classifier classifies each feature set by an adaptive method. The proposed ensemble classifier combines the output of the three simple classifiers, which are essentially the same. Although these simple classifiers looks the same, but the different input feature set and the adaptive thresholds related to each classifier makes them to be different with each other. Therefore, from the viewpoint of the classifiers combination, the proposed method can be considered as a feature level combination type. The proposed method was evaluated by applying on two datasets: Persian and South African signature datasets. Experimental results shown our proposed method has the lowest error rate in comparison with other methods.

목차

Abstract
 1. Introduction
 2. Related Works
 3. The Proposed System
  3.1. Pre-Processing
  3.2. Feature Extraction
  3.3. Classification
 4. Experimental Results
  4.1. Experiments on the Simple Classifiers
  4.2. Experiments on the Ensemble Classifier
  4.3. Comparison with Other Methods
 5. Conclusion and Future Works
 References

키워드

저자

• Mohamad Hoseyn Sigari [ Machine Vision Research Lab., Computer Engineering Department, College of Eng. Ferdowsi University of Mashhad, Control and Intelligent Processing Center of Excellence (CIPCE) Electrical and Computer Engineering Department, College of Engineering University of Tehran ]
• Muhammad Reza Pourshahabi [ Machine Vision Research Lab., Computer Engineering Department, College of Eng. Ferdowsi University of Mashhad ]
• Hamid Reza Pourreza [ Machine Vision Research Lab., Computer Engineering Department, College of Eng. Ferdowsi University of Mashhad ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605