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Signature authentication based on human intervention: performance and complementarity with automatic systems

Signature authentication based on human intervention: performance and complementarity with automatic systems

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This work explores human intervention to improve Automatic Signature Verification (ASV). Significant efforts have been made in order to improve the performance of ASV algorithms over the last decades. This work analyzes how human actions can be used to complement automatic systems. Which actions to take and to what extent those actions can help state-of-the-art ASV systems is the final aim of this research line. The analysis at classification level comprises experiments with responses from 500 people based on crowdsourcing signature authentication tasks. The results allow to establish a human baseline performance and comparison with automatic systems. Intervention at feature extraction level is evaluated using a self-developed tool for the manual annotation of signature attributes inspired in Forensic Document Experts analysis. We analyze the performance of attribute-based human signature authentication and its complementarity with automatic systems. The experiments are carried out over a public database including the two most popular signature authentication scenarios based on both online (dynamic time sequences including position and pressure) and offline (static images) information. The results demonstrate the potential of human interventions at feature extraction level (by manually annotating signature attributes) and encourage to further research in its capabilities to improve the performance of ASV.

References

    1. 1)
      • R. Plamondon , S.N. Srihari .
        1. Plamondon, R., Srihari, S.N.: ‘On-line and off-line handwriting recognition: a comprehensive survey’, IEEE Trans. Pattern Anal. Mach. Intell., 2000, 22, pp. 6384.
        . IEEE Trans. Pattern Anal. Mach. Intell. , 63 - 84
    2. 2)
      • D. Impedovo , G. Pirlo .
        2. Impedovo, D., Pirlo, G.: ‘Automatic signature verification: the state of the art’, IEEE Trans. Syst. Man Cybern. C, 2008, 38, (5), pp. 609635.
        . IEEE Trans. Syst. Man Cybern. C , 5 , 609 - 635
    3. 3)
      • J. Fierrez , J. Ortega-Garcia . (2008)
        3. Fierrez, J., Ortega-Garcia, J.: ‘On-line signature verification’, in Jain, A.K., Ross, A., Flynn, P. (EDs.): ‘Handbook of biometrics’, (Springer, New York, NY 10013, USA, 2008), pp. 189209.
        .
    4. 4)
      • N. Kumar , A.C. Berg , P.N. Belhumeur .
        4. Kumar, N., Berg, A.C., Belhumeur, P.N., et al: ‘Describable visual attributes for face verification and image search’, IEEE Trans. Pattern Anal. Mach. Intell., 2011, 33, (10), pp. 19621977.
        . IEEE Trans. Pattern Anal. Mach. Intell. , 10 , 1962 - 1977
    5. 5)
      • D. Reid , M. Nixon , S.V. Stevenage .
        5. Reid, D., Nixon, M., Stevenage, S.V.: ‘Soft biometrics; human identification using comparative descriptions’, IEEE Trans. Pattern Anal. Mach. Intell., 2014, 36, (6), pp. 12161228.
        . IEEE Trans. Pattern Anal. Mach. Intell. , 6 , 1216 - 1228
    6. 6)
      • B.F. Klare , S. Klum , J. Klontz .
        6. Klare, B.F., Klum, S., Klontz, J., et al: ‘Suspect identification based on descriptive facial attributes’. Proc. of Int. Joint Conf. on Biometrics, Clearwater, FL, USA, 2014, pp. 18.
        . Proc. of Int. Joint Conf. on Biometrics , 1 - 8
    7. 7)
      • P. Samangouei , V.M. Patel , R. Chellappa .
        7. Samangouei, P., Patel, V.M., Chellappa, R.: ‘Continuous user authentication on mobile devices based on facial attributes’, IEEE Signal Process. Mag., 2016, 33, (4), pp. 4961.
        . IEEE Signal Process. Mag. , 4 , 49 - 61
    8. 8)
      • P. Tome , J. Fierrez , R. Vera-Rodriguez .
        8. Tome, P., Fierrez, J., Vera-Rodriguez, R., et al: ‘Soft biometrics and their application in person recognition at a distance’, IEEE Trans. Inf. Forensics Sec., 2014, 9, (3), pp. 464475.
        . IEEE Trans. Inf. Forensics Sec. , 3 , 464 - 475
    9. 9)
      • L. Best-Rowden , S. Bisht , J.C. Klontz .
        9. Best-Rowden, L., Bisht, S., Klontz, J.C., et al: ‘Unconstrained face recognition: establishing baseline human performance via crowdsourcing’. Proc. of the Int. Joint Conf. on Biometrics, Tampa, USA, 2014, pp. 16.
        . Proc. of the Int. Joint Conf. on Biometrics , 1 - 6
    10. 10)
      • H. Han , C. Otto , X. Liu .
        10. Han, H., Otto, C., Liu, X., et al: ‘Demographic estimation from face images: human vs. machine performance’, IEEE Trans. Pattern Anal. Mach. Intell., 2015, 37, (6), pp. 11481161.
        . IEEE Trans. Pattern Anal. Mach. Intell. , 6 , 1148 - 1161
    11. 11)
      • J. Coetzer , B.M. Herbst , J.A. Du Preez .
        11. Coetzer, J., Herbst, B.M., Du Preez, J.A.: ‘Off-line signature verification: a comparison between human and machine performance’. Proc. Tenth Int. Workshop on Frontiers in Handwriting Recognition, La Baule, France, 2006, pp. 481485.
        . Proc. Tenth Int. Workshop on Frontiers in Handwriting Recognition , 481 - 485
    12. 12)
      • P.J. Phillips , M.Q. Hill , J.A. Swindle .
        12. Phillips, P.J., Hill, M.Q., Swindle, J.A., et al: ‘Human and algorithm performance on the PaSC face recognition challenge’. Proc. Int. Conf. on Biometrics: Theory, Applications and Systems, Arlington, USA, 2015, pp. 18.
        . Proc. Int. Conf. on Biometrics: Theory, Applications and Systems , 1 - 8
    13. 13)
      • D. Morocho , A. Morales , J. Fierrez .
        13. Morocho, D., Morales, A., Fierrez, J., et al: ‘Towards human-assisted signature recognition: improving biometric systems through attribute-based recognition’. Proc. IEEE Int. Conf. on Identity, Security and Behavior Analysis, Japan, 2016, pp. 16.
        . Proc. IEEE Int. Conf. on Identity, Security and Behavior Analysis , 1 - 6
    14. 14)
      • A.K. Jain , S.C. Dass , K. Nandakumar .
        14. Jain, A.K., Dass, S.C., Nandakumar, K., et al: ‘Soft biometric traits for personal recognition systems’. Proc. Int. Conf. Biometric Authentication, Hong Kong, 2004, pp. 731738.
        . Proc. Int. Conf. Biometric Authentication , 731 - 738
    15. 15)
      • A. Dantcheva , C. Velardo , A. D'angelo .
        15. Dantcheva, A., Velardo, C., D'angelo, A., et al: ‘Bag of soft biometrics for person identification: new trends and challenges’, Mutimedia Tools Appl., 2010, 10, pp. 136.
        . Mutimedia Tools Appl. , 1 - 36
    16. 16)
      • L. Oliveira , E. Justino , C. Freitas .
        16. Oliveira, L., Justino, E., Freitas, C., et al: ‘The graphology applied to signature verification’. Proc. 12th Conf. of the Int. Graphonomics Society, Salerno, Italy, 2005, pp. 286290.
        . Proc. 12th Conf. of the Int. Graphonomics Society , 286 - 290
    17. 17)
      • T.M. Burkes , D.P. Seiger , D. Harrison .
        17. Burkes, T.M., Seiger, D.P., Harrison, D.: ‘Handwriting examination: meeting the challenges of science and the law’, Forensic Sci. Commun., 2009, 11, (4).
        . Forensic Sci. Commun. , 4
    18. 18)
      • M.I. Malik , M. Liwicki , A. Dengel .
        18. Malik, M.I., Liwicki, M., Dengel, A., et al: ‘Man vs. machine: a comparative analysis for forensic signature verification’. Proc. of the 16th Int. Graphonomics Society Conf., 2013, pp. 913.
        . Proc. of the 16th Int. Graphonomics Society Conf. , 9 - 13
    19. 19)
      • M.I. Malik , M. Liwicki , A. Dengel .
        19. Malik, M.I., Liwicki, M., Dengel, A.: ‘Part-based automatic system in comparison to human experts for forensic signature verification’. Proc. Int. Conf. on Document Analysis and Recognition, Washington, DC, USA, 2013, pp. 872876.
        . Proc. Int. Conf. on Document Analysis and Recognition , 872 - 876
    20. 20)
      • H. Coetzer , R. Sabourin .
        20. Coetzer, H., Sabourin, R.: ‘A human-centric off-line signature verification system’. Proc. Int. Conf. on Document Analysis and Recognition, Curitiba, Brazil, 2007, pp. 153157.
        . Proc. Int. Conf. on Document Analysis and Recognition , 153 - 157
    21. 21)
      • D. Morocho , A. Morales , J. Fierrez .
        21. Morocho, D., Morales, A., Fierrez, J., et al: ‘Signature recognition: establishing human performance via crowdsourcing’. Proc. Fourth Int. Workshop on Biometrics and Forensics, Limassol, Cyprus, 2016, pp. 16.
        . Proc. Fourth Int. Workshop on Biometrics and Forensics , 1 - 6
    22. 22)
      • J. Coetzer , J. Swanepoel , R. Sabourin .
        22. Coetzer, J., Swanepoel, J., Sabourin, R.: ‘Efficient cost-sensitive human-machine collaboration for offline signature verification’, IS&T/SPIE Electron. Imaging, 2012, 8297, pp. 18.
        . IS&T/SPIE Electron. Imaging , 1 - 8
    23. 23)
      • J. Fierrez , J. Galbally , J. Ortega-Garcia .
        23. Fierrez, J., Galbally, J., Ortega-Garcia, J., et al: ‘BiosecurID: a multimodal biometric database’, Pattern Anal. Appl., 2010, 13, (2), pp. 235246.
        . Pattern Anal. Appl. , 2 , 235 - 246
    24. 24)
      • M. Martinez-Diaz , J. Fierrez , R.P. Krish .
        24. Martinez-Diaz, M., Fierrez, J., Krish, R.P., et al: ‘Mobile signature verification: feature robustness and performance comparison’, IET Biometrics, 2014, 3, pp. 267277.
        . IET Biometrics , 267 - 277
    25. 25)
      • J. Galbally , M. Diaz-Cabrera , M.A. Ferrer .
        25. Galbally, J., Diaz-Cabrera, M., Ferrer, M.A., et al: ‘On-line signature recognition through the combination of real dynamic data and synthetically generated static data’, Pattern Recognit., 2015, 48, pp. 29212934.
        . Pattern Recognit. , 2921 - 2934
    26. 26)
      • M. Martinez-Diaz , J. Fierrez . (2015)
        26. Martinez-Diaz, M., Fierrez, J.: ‘Signature databases and evaluation’, in Li, S.Z., Jain, A.K. (EDs.): ‘Encyclopedia of biometrics’ (Springer, New York, NY 10013, USA, 2015), pp. 13671375.
        .
    27. 27)
      • M.I. Malik , M. Liwicki , L. Alewijnse .
        27. Malik, M.I., Liwicki, M., Alewijnse, L., et al: ‘ICDAR2013 competitions on signature verification and writer identification for on- and offline skilled forgeries (SigWiComp2013)’. Proc. of Int. Conf. on Document Analysis and Recognition, Tunisia, 2013, pp. 11081114.
        . Proc. of Int. Conf. on Document Analysis and Recognition , 1108 - 1114
    28. 28)
      • N. Houmani , A. Mayoue , S. Garcia-Salicetti .
        28. Houmani, N., Mayoue, A., Garcia-Salicetti, S., et al: ‘Biosecure signature evaluation campaign (BSEC2009): evaluating online signature algorithms depending on the quality of signatures’, Pattern Recognit., 2012, 45, pp. 9931003.
        . Pattern Recognit. , 993 - 1003
    29. 29)
      • M. Ferrer , J. Vargas , A. Morales .
        29. Ferrer, M., Vargas, J., Morales, A., et al: ‘Robustness of offline signature verification based on gray level features’, IEEE Trans. Inf., Forensics Sec., 2012, 7, (3), pp. 966977.
        . IEEE Trans. Inf., Forensics Sec. , 3 , 966 - 977
    30. 30)
      • A.K. Jain , K. Nandakumar , A. Ross .
        30. Jain, A.K., Nandakumar, K., Ross, A.: ‘Score normalization in multimodal biometric systems’, Pattern Recognit., 2005, 38, (12), pp. 22702285.
        . Pattern Recognit. , 12 , 2270 - 2285
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