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.