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Learning Discriminative Feature Hierarchies for Off-Line Signature Verification

Handwritten signature is the most widespread and socially accepted method for personal authentication. Therefore automatic handwritten signature verification (HSV) is a task of realistic significance and a popular research topic in the field of pattern recognition. In this paper, we propose to learn discriminative feature hierarchies using supervised convolutional neural networks (CNN) to improve the off-line HSV performance. The feature space is modeled at both the global and local levels, combines clues from both shallow and deep representations, and is expected to capture intrinsic properties of handwritten signatures. Writer-dependent support vector machines (SVM) are trained based on the learned features for verification. Experimental results show that our method achieves competitive performance on two benchmark data sets, namely the MCYT-75 data set and the CEDAR data set.