Name
Abstract | ||
|---|---|---|
Signature verification, if we consider the muscle memory, is a biometric for identification technology. To access muscle memory, we use a motion sensor that consists of accelerometer and gyroscope to implement a signature verification system. The motion sensor records six motion values including three-axis accelerations and angular velocities while name signing. 14 features of signature are extracted from the sequence of accelerations and angular velocities. A support vector machine (SVM) is then applied to verify the signatures. The proposed method was applied to verify the Chinese signatures. The SVM is trained by the training data from each person. The true positive rate of the proposed method can reach to 95.66%. Fake signatures generated by tracing from true signatures can also be recognized by the proposed method.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3341162.3343840 | Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers |
Keywords | Field | DocType |
SVM, handwritten signature verification, machine learning, motion sensor | Computer vision,Identification technology,Gyroscope,Muscle memory,Accelerometer,Computer science,Support vector machine,Artificial intelligence,Motion sensors,Biometrics,Tracing | Conference |
ISBN | Citations | PageRank |
978-4503-6869-8 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chang-Chieh Cheng | 1 | 1 | 1.36 |
| Yi-Chi Chen | 2 | 3 | 2.39 |
| Yu-Tai Ching | 3 | 2 | 2.41 |