## 6 Conclusions and Future Work  
This paper proposed a new feature-extraction method using CNN for dynamic signature verification. A CNN was exploited to generate an _S-vector_ as the input to the autoencoder. Experimental results showed that the CNN trained with forged and genuine signatures could generate an _S-vector_ that achieved good accuracy in distinguishing forged signatures but did not perform well in other tests, namely, subject/others and time difference. Therefore, we combined the _S-vector_ of two CNN feature extractors that were trained for different purposes, which yielded good results in all the three types of tests. Using the proposed method, the EER was improved by up to 1.1% (subject/others), 3.2% (time difference), and 13.7% (skilled forgery) compared to that in the previous work. The proposed dynamic signature verification system could be used for secondary authentication such as in smartphones, PDAs, mobile computers, financial settlements, and mobile banking. In future work, we plan to verify signatures drawn by a hand gripping a smartphone, i.e., a user draws a signature in the air by a hand gripping a smartphone, and not on a screen by a finger. We expect that the large signature drawn by hand could have positive effects of verification accuracy and user convenience. However, the features and characteristics of the hand-drawn signature might be different from those of a finger-drawn signature. The differences could bring up some issues. This work was supported by the Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korean government (MSIT) (No. 2016-0-00110, Development of Information Security Core Technology and 2016-0-00173, Security Technologies for Financial Fraud Prevention on Fintech). The authors contributed equally to this work. The authors declare no conflict of interest. +++ ==WARNING: Truncated because of repetitions==
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