BoVW Based Feature Selection for Uyghur Offline Signature Verification
As an important research direction in the field of biometrics, offline signature verification plays an important role. This paper proposes BoVW based on feature selection algorithm MRMR for offline signature verification. In this paper, eigenvectors were formed by extracting visual word features and the features were obtained by building a visual word bag of signature samples. In order to improve the relevance between eigenvectors and categories, and reduce the redundancy between features, the Maximum Relevance and Minimum Redundancy algorithm was used to select features of visual word eigenvectors. The algorithm can find the optimal feature subset. The experiments were conducted using 1200 samples from in our Uyghur signature database, and comparison experiments were carried on selecting 2640 samples from CEDAR database. It was obtained 93.81% of ORR from Uyghur signature and 95.38% of ORR using Latin signature from CEADER database respectively. The experimental results indicated the efficiency of proposed method in this paper.
KeywordsOffline signature verification BoVW MRMR Feature selection
This work was supported by the National Natural Science Foundation of China (No. 61363064, 61563052, 61163028).
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