TY - GEN
T1 - Signature verification with chain code and geometric features
AU - Kongtongvattana, Chayun
AU - Phienthrakul, Tanasanee
N1 - Publisher Copyright:
© 2017 ACM.
PY - 2017/2/24
Y1 - 2017/2/24
N2 - Signature is a kind of biometric identification that is widely used in contracts, agreements, and other legal documents. However, the signature may be imitated by deceivers. In order to verify the signature, specialists are required and they may use a lot of time to inspect the suspect signatures. This paper proposes to verify the signatures using machine learning techniques, such as neural network, decision tree, decision table, and Naïve Bayes. Feature extraction is an important part in learning process. Concept of chain code is introduced and this concept will be used for extracting some features from signature images. These features and geometric features are used to train and test in order to verify the actual signatures. The experimental results show that the proposed features can improve the accuracy of signature verification.
AB - Signature is a kind of biometric identification that is widely used in contracts, agreements, and other legal documents. However, the signature may be imitated by deceivers. In order to verify the signature, specialists are required and they may use a lot of time to inspect the suspect signatures. This paper proposes to verify the signatures using machine learning techniques, such as neural network, decision tree, decision table, and Naïve Bayes. Feature extraction is an important part in learning process. Concept of chain code is introduced and this concept will be used for extracting some features from signature images. These features and geometric features are used to train and test in order to verify the actual signatures. The experimental results show that the proposed features can improve the accuracy of signature verification.
KW - Chain code
KW - Classification
KW - Geometric feature
KW - Neural network
KW - Signature verification
UR - https://www.scopus.com/pages/publications/85025159322
U2 - 10.1145/3055635.3056642
DO - 10.1145/3055635.3056642
M3 - Conference contribution
AN - SCOPUS:85025159322
T3 - ACM International Conference Proceeding Series
SP - 342
EP - 346
BT - Proceedings of 2017 9th International Conference on Machine Learning and Computing, ICMLC 2017
PB - Association for Computing Machinery
T2 - 9th International Conference on Machine Learning and Computing, ICMLC 2017
Y2 - 24 February 2017 through 26 February 2017
ER -