Forged Signature Distinction Using Convolutional Neural Network for Feature Extraction
Source abstract
Abstract
This paper proposes a dynamic verification scheme for finger-drawn signatures in smartphones. As a dynamic feature, the movement of a smartphone is recorded with accelerometer sensors in the smartphone, in addition to the moving coordinates of the signature. To extract high-level longitudinal and topological features, the proposed scheme uses a convolution neural network (CNN) for feature extraction, and not as a conventional classifier. We assume that a CNN trained with forged signatures can extract effective features (called S-vector), which are common in forging activities such as hesitation and delay before drawing the complicated part. The proposed scheme also exploits an autoencoder (AE) as a classifier, and the S-vector is used as the input vector to the AE. An AE has high accuracy for the one-class distinction problem such as signature verification, and is also greatly dependent on the accuracy of input data. S-vector is valuable as the input of AE, and, consequently, could lead to improved verification accuracy especially for distinguishing forged signatures. Compared to the previous work, i.e., the MLP-based finger-drawn signature verification scheme, the proposed scheme decreases the equal error rate by 13.7%, specifically, from 18.1% to 4.4%, for discriminating forged signatures.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Handwritten Text Recognition Techniques · Hand Gesture Recognition Systems · Advanced Neural Network Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Convergence Science, Kongju National University, Chungnam 32588, Korea
Department of Medical Information, Kongju National University, Chungnam 32588, Korea
References (29)
Show source-provided references
1. Roh, J.H., Lee, S.H., and Kim, S. (2016, January 19–21). Keystroke Dynamics for Authentication in Smartphone. Proceedings of the International Conference Information and Communication Technology (ICTC), Jeju Island, Korea.10.1109/ICTC.2016.7763394 ↗
2. Liu, D., Dong, B., Gao, X., and Wang, H. (2015, January 2–5). Exploiting Eye Tracking for Smartphone Authentication. Proceedings of the International Conference on Applied Cryptography and Network Security (ACNS), New York, NY, USA.10.1007/978-3-319-28166-7_22 ↗
3. Buriro, A., Crispo, B., Delfrari, F., and Wrona, K. (2016, January 22–26). Hold and Sign: A Novel Behavioral Biometrics for Smartphone User Authentication. Proceedings of the IEEE Symposium on Security and Privacy Workshops (SPW), San Jose, CA, USA.10.1109/SPW.2016.20 ↗
4. Online signature verification using a new extreme points warping technique10.1016/S0167-8655(03)00155-7 ↗
5. Fierrez-Aguilar, J., Nanni, L., Lopez-Peñalba, J., Ortega-Garcia, J., and Maltoni, D. (2005, January 20–22). An on-line signature verification system based on fusion of local and global information. Proceedings of the Audio- and Video-Based Biometric Person Authentication (AVBPA), Rye Brook, NY, USA.10.1007/11527923_54 ↗
6. An advanced multi-matcher method for on-line signature verification featuring global features and tokenised random numbers10.1016/j.neucom.2006.02.009 ↗
7. Online signature verification and recognition: An approach based on symbolic representation10.1109/TPAMI.2008.302 ↗
8. Online signature verification with support vector machines based on LCSS kernel functions10.1109/TSMCB.2009.2034382 ↗
9. Iranmanesh, V., Ahmad, S.M.S., Adnan, W.A.W., Malallah, F.L., and Yussof, S. (2014, January 26–28). Online signature verification using neural network and Pearson correlation features. Proceedings of the IEEE Conference on Open Systems (ICOS), Subang, Selangor, Malaysia.10.1109/ICOS.2013.6735040 ↗
10. Fayyaz, M., Saffar, M.H., Sabokrou, M., Hoseini, M., and Fathy, M. (2015, January 3–5). Online signature verification based on feature representation. Proceedings of the International Symposium on Artificial Intelligence and Signal Processing (AISP), Mashhad, Iran.10.1109/AISP.2015.7123528 ↗
11. Online signature verification on mobile devices10.1109/TIFS.2014.2316472 ↗
12. Antal, M., and Szabó, L.Z. (2016, January 12–14). On-line verification of finger drawn signatures. Proceedings of the International Symposium on Applied Computational Intelligence and Informatics (SACI), Timisoara, Romania.10.1109/SACI.2016.7507413 ↗
13. Paudel, N., Querini, M., and Italiano, G.F. (2016, January 19–21). Handwritten Signature Verification for Mobile Phones. Proceedings of the International Conference on Information Systems Security and Privacy (ICISSP), Rome, Italy.10.5220/0005675200460052 ↗
14. Zhang, C., Gao, W., Song, J., and Jiang, J. (2016, January 14–16). An imbalanced data classification algorithm of improved autoencoder neural network. Proceedings of the IEEE International Conference on Advanced Computational Intelligence (ICACI), Chiang Mai, Thailand.10.1109/ICACI.2016.7449810 ↗
15. Nguyen, T.V., Sae-Bae, N., and Memon, N. (2014, January 27–30). Finger-drawn PIN Authentication on Touch Devices. Proceedings of the IEEE International Conference on Image Process (ICIP), Paris, France.
16. LeCun, Y., and Bengio, Y. (1998). Convolutional networks for images, speech, and time series. The Handbook of Brain Theory and Neural Networks, MIT Press.
17. Simonyan, K., and Zisserman, A. (2015, January 7–9). Very deep convolutional networks for large-scale image recognition. Proceedings of the International Conference on Learning Representations (ICLR), San Diego, CA, USA.
18. Marchi, E., Vesperini, F., Eyben, F., Squartini, S., and Schuller, B. (2015, January 19–24). A novel approach for automatic acoustic novelty detection using a denoising autoencoder with bidirectional LSTM neural networks. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), South Brisbane, QLD, Australia.10.1109/ICASSP.2015.7178320 ↗
19. Mobile Finger Signature Verification Robust to Skilled Forgery
20. Online signature verification based on DCT and sparse representation10.1109/TCYB.2014.2375959 ↗
21. Fischer, A., Diaz, M., Plamondon, R., and Ferrer, M.A. (2015, January 23–26). Robust score normalization for DTW-based on-line signature verification. Proceedings of the International Conference on Document Analysis and Recognition (ICDAR), Nancy, France.10.1109/ICDAR.2015.7333760 ↗
22. Wijesoma, W.S., Mingming, M., and Yue, K.W. (2001, January 1–3). On-line signature verification using a computational intelligence approach. Proceedings of the Fuzzy Days, Dortmund, Germany.
23. A Robust On-line Signature Verification System10.5391/IJFIS.2003.3.1.027 ↗
24. HMM-based on-line signature verification: Feature extraction and signature modeling10.1016/j.patrec.2007.07.012 ↗
25. Blunsom, P., Grefenstette, E., and Kalchbrenner, N. (2014, January 22–27). A convolutional neural network for modelling sentences. Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL), Baltimore, MD, USA.
26. Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3–6). Imagenet classification with deep convolutional neural networks. Proceedings of the Conference on Neural Information Processing Systems (NIPS), Stateline, NV, USA.
27. Dauphin, Y., de Vries, H., and Bengio, Y. (2015, January 7–12). Equilibrated adaptive learning rates for non-convex optimization. Proceedings of the Conference on neural information processing systems (NIPS), Montréal, QC, Canada.
28. (2017, December 02). Theano 1.0 Release. Available online: http://deeplearning.net/software/theano/.
29. Customized Data Filtering For Mobile Signature Verification