Effectiveness of Pen Pressure, Azimuth, and Altitude Features for Online Signature Verification

  • Daigo Muramatsu
  • Takashi Matsumoto
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4642)


Many algorithms for online signature verification using multiple features have been proposed. Recently it has been argued that pen pressure, azimuth, and altitude can cause instability and deteriorate the performance. Algorithms without pen pressure and inclination features outperformed with them in SVC2004. However, we previously found that these features improved the performance in evaluations using our private database. The effectiveness of the features thus depended on the algorithm. Therefore, we re-evaluated our algorithm using the same database as used in SVC2004 and discuss the effectiveness of pen pressure, azimuth and altitude. Experimental results show that even though these features are not so effective when they are used by themselves, they improved the performance when used in combination with other features. When pen pressure and inclination features were considered, an EER of 3.61% was achieved, compared to an EER of 5.79% when these features were not used.


Online signature verification Pen pressure Pen azimuth Pen altitude Fusion SVC2004 


  1. 1.
    Plamondon, R., Lorette, G.: Automatic signature verification and writer identification - The state of the art. Pattern Recognition 22(2), 101–131 (1989)CrossRefGoogle Scholar
  2. 2.
    Martens, R., Claesen, L.: Incorporating local consistency information into the online signature verification process. IJDAR 1(2), 110–115 (1998)CrossRefGoogle Scholar
  3. 3.
    Shimizu, H., Kiyono, S., Motoki, T., Gao, W.: An electrical pen for signature verification using a two-dimensional optical angle sensor. Sensor and Actuators A111, 211–216 (2004)Google Scholar
  4. 4.
    Hook, C., Kempf, J., Scharfenberg, G.: A Novel digitizing pen for the analysis of pen pressure and inclination in handwriting biometrics. In: Maltoni, D., Jain, A.K. (eds.) BioAW 2004. LNCS, vol. 3087, pp. 283–294. Springer, Heidelberg (2004)Google Scholar
  5. 5.
    Munich, M.E., Perona, P.: Visual identification by signature tracking. IEEE Trans. Pattern Anal. and Machine Intell. 25(2), 200–217 (2003)CrossRefGoogle Scholar
  6. 6.
    Garcia-Salicetti, S., Beumier, C., Chollet, G., Dorizzi, B., Leroux-Les Jardins, J., Lunter, J., Ni, Y., Petrovska-Delacretaz, D.: BIOMET: a multimodal person authentication database including face, voice, fingerprint, hand and signature modalities. In: Kittler, J., Nixon, M.S. (eds.) AVBPA 2003. LNCS, vol. 2688, pp. 845–853. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  7. 7.
    Ortega-Garcia, J., Fierrez-Aguilar, J., Simon, D., Gonzalez, J., Faundez-Zanuy, M., Espinosa, V., Satue, A., Hernaez, I., Igarza, J.-J., Vivaracho, C., Escudero, D., Moro, Q.-I.: MCYT baseline corpus: a bimodal biometric database. IEE Proceedings Vision, Image and Signal Processing 150(6), 395–401 (2003)CrossRefGoogle Scholar
  8. 8.
    Yeung, D.-Y., Chang, H., Xiong, Y., George, S., Kashi, R., Matsumoto, T., Rigoll, G.: SVC. First international signature verification competition. In: Zhang, D., Jain, A.K. (eds.) ICBA 2004. LNCS, vol. 3072, pp. 16–22. Springer, Heidelberg (2004)Google Scholar
  9. 9.
    Ortega-Garcia, J., Fierrez-Aguilar, J., Martin-Rello, J., Gonzalez-Rodriguez, J.: Complete signal modeling and score normalization for function-based dynamic signature verification. In: Kittler, J., Nixon, M.S. (eds.) AVBPA 2003. LNCS, vol. 2688, pp. 658–667. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  10. 10.
    Van Ly, B., Garcia-Salicetti, S., Dorizzi, B.: Fusion of HMM’s likelihood and viterbi path for on-line signature verification. In: Maltoni, D., Jain, A.K. (eds.) BioAW 2004. LNCS, vol. 3087, pp. 318–331. Springer, Heidelberg (2004)Google Scholar
  11. 11.
    Hongo, Y., Muramatsu, D., Matsumoto, T.: Modification on intersession variability in on-line signature verifier. In: Kanade, T., Jain, A., Ratha, N.K. (eds.) AVBPA 2005. LNCS, vol. 3546, pp. 455–463. Springer, Heidelberg (2005)Google Scholar
  12. 12.
    Muramatsu, D., Kondo, M., Sasaki, M., Tachibana, S., Matsumoto, T.: A Markov chain Monte Carlo algorithm for bayesian dynamic signature verification. IEEE Trans. Information Forensics and Security 1(1), 22–34 (2006)CrossRefGoogle Scholar
  13. 13.
    Marcos Faundez-Zanuy, M.: On-line signature recognition based on VQ-DTW. Pattern Recognition 40, 981–992 (2007)zbMATHCrossRefGoogle Scholar
  14. 14.
    Muramatsu, D., Matsumoto, T.: Online signature verification using user generic fusion model. IEICE Trans. J90-D(2), 450–459 (2007)Google Scholar
  15. 15.
    Kholmatov, A., Yanikoglu, B.: Identity authentication using improved online signature verification method. Pattern Recognition Letters 26(15), 2400–2408 (2005)CrossRefGoogle Scholar
  16. 16.
    Fierrez-Aguilar, J., Nannil, L., Lopez-Peñalba, J., Ortega-Garcia, J., Maltoni, D.: An on-line signature verification system based on fusion of local and global information. In: Kanade, T., Jain, A., Ratha, N.K. (eds.) AVBPA 2005. LNCS, vol. 3546, pp. 523–532. Springer, Heidelberg (2005)Google Scholar
  17. 17.
    Lei, H., Govindaraju, V.: A comparative study on the consistency of features in on-line signature verification. Pattern Recognition Letters 26(15), 2483–2489 (2005)CrossRefGoogle Scholar
  18. 18.
    Taguchi, H., Kiriyama, K., Tanaka, E., Fujii, K.: On-line recognition of handwritten signature by feature extraction of the pen movements (Japanese). IEICE Trans. J71-D(5), 830–840 (1988)Google Scholar
  19. 19.
    Komiya, Y., Ohishi, T., Matsumoto, T.: A pen input on-line signature verifier integration position, pressure and inclination trajectories. IEICE Trans. INF. & SYST. E84-D(7), 833–838 (2001)Google Scholar
  20. 20.
    Hangai, S., Yamanaka, S., Hanamoto, T.: On-line signature verification based on altitude and direction of pen movement. In: Proc. ICME 2000, vol. 1, pp. 489–492 (2000)Google Scholar
  21. 21.
    Lee, L.L., Berger, T., Aviczer, E.: Reliable on-line human signature verification systems. IEEE Trans. Pattern Anal. and Machine Intell. 18(6), 643–647 (1996)CrossRefGoogle Scholar
  22. 22.
    Nalwa, V.S.: Automatic on-line signature verification. Proc. IEEE 85(2), 215–239 (1997)CrossRefGoogle Scholar
  23. 23.
    Jain, A.K., Griess, F.D., Connell, S.D.: On-line signature verification. Pattern Recognition 35(12), 2963–2972 (2002)zbMATHCrossRefGoogle Scholar
  24. 24.
    Fierrez-Aguilar, J., Krawczyk, S., Ortega-Garcia, J., Jain, A.K.: Fusion of local and regional approaches for on-line signature verification. In: Li, S.Z., Sun, Z., Tan, T., Pankanti, S., Chollet, G., Zhang, D. (eds.) IWBRS 2005. LNCS, vol. 3781, pp. 188–196. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  25. 25.
    Maio, D., Maltoni, D., Cappelli, R., Wayman, J.L., Jain, A.K.: FVC2000: Fingerprint verification competition. IEEE Trans. on Pattern Anal. Machine Intell. 24(3), 402–412 (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Daigo Muramatsu
    • 1
  • Takashi Matsumoto
    • 2
  1. 1.Department of Electrical and Mechanical Engineering, Seikei University, 3-3-1 Kichijoji-kitamachi, Musashino-shi, Tokyo 180-8633Japan
  2. 2.Department of Electrical Engineering and Bioscience, Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555Japan

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