Online signature verification based on feature representation

Signature verification techniques employ various specifications of a signature. Feature extraction and feature selection have an enormous effect on accuracy of signature verification. Feature extraction is a difficult phase of signature verification systems due to different shapes of signatures and different situations of sampling. This paper presents a method based on feature learning, in which a sparse autoencoder tries to learn features of signatures. Then learned features have been employed to present users´ signatures. Finally, users´ signatures have been classified using one-class classifiers. The proposed method is signature shape independent thanks to learning features from users´ signatures using autoencoder. Verification process of proposed system is evaluated on SVC2004 signature database, which contains genuine and skilled forgery signatures. The experimental results indicate error reduction and accuracy enhancement.