An Ensemble Classifier Approach for Static Signature Verification Based on Multi-Resolution Extracted Features

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.