**Yu Jia, Linlin Huang \* and Houjin Chen**

School of Electronic and Information Engineering, Beijing Jiaotong University, No. 3 Shangyuancun Haidian District, Beijing 100044, China; 16120010@bjtu.edu.cn (Y.J.); hjchen@bjtu.edu.cn (H.C.)

**\*** Correspondence: huangll@bjtu.edu.cn; Tel.: +86-010-5168-8206

† This paper is an extended version of our paper published in PRCV 2018: Chinese Conference on Pattern Recognition and Computer Vision, Guangzhou, China, 23–26 November 2018.

Received: 18 March 2019; Accepted: 13 April 2019; Published: 16 April 2019

**Abstract:** As a behavioral biometric trait, an online signature is extensively used to verify a person's identity in many applications. In this paper, we present a method using shape contexts and function features as well as a two-stage strategy for accurate online signature verification. Specifically, in the first stage, features of shape contexts are extracted from the input and classification is made based on distance metric. Only the inputs passing by the first stage are represented by a set of function features and verified. To improve the matching accuracy and efficiency, we propose shape context-dynamic time warping (SC-DTW) to compare the test signature with the enrolled reference ones based on the extracted function features. Then, classification based on interval-valued symbolic representation is employed to decide if the test signature is a genuine one. The proposed method is evaluated on SVC2004 Task 2 achieving an Equal Error Rate of 2.39% which is competitive to the state-of-the-art approaches. The experiment results demonstrate the effectiveness of the proposed method.

**Keywords:** online signature verification; shape contexts; function features; SC-DTW; symbolic representation; two-stage method
