J4 ›› 2017, Vol. 34 ›› Issue (1): 23-31.

• Image and Information Proc. • Previous Articles     Next Articles

Handwritten signature verification algorithm based on LBP and deep learning

MA Xiaoqing, SANG Qingbing   

  1. School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China
  • Received:2016-01-15 Revised:2016-03-01 Published:2017-01-28 Online:2017-01-28
  • About author:马小晴(1989-),女,河北辛集人,研究生,研究方向为手写签名识别。Email:xiaoqingxjzx@126.com;

Abstract: In order to improve the performance of handwritten signature verification algorithm, a handwritten signature verification algorithm based on local binary pattern (LBP) feature and deep learning is presented. Aiming at signature image, preprocessing and Wiener filtering are used to get rid of noise. The preprocessed signature image was divided into 3×4 blocks and LBP is used to each sub-block. The texture histogram characteristics of each sub-block are connected to form a global histogram characteristics. The feature vectors obtained are used as inputs of deep belief network (DBN) , DBN is trained layer by layer, and the classification plane is formed at the top to recognize the signature image. Experiments are conducted on GPDS,MCYT and the original database, and the recognition error rates are 5.85%, 9.3% and 1.17%, respectivly. The handwritten signature recognition accuracy is effectively improved, which meets the requirements of practical application.

Key words: image processing; handwritten signature verification; deep learning; deep belief network ; local binary pattern