Abstract
This works presents a method to verify a person identity based on off-line handwritten strokes analysis. Its main contribution is that the descriptors are obtained from the constitutive segments of each grapheme, in contrast with the complexity of the handwritten images used in signature recognition system or even with the graphemes themselves. In this way, only few handwriting samples taken from a short text could be enough to identify the writer. The descriptor is based on an estimation of the pressure of the stroke grayscale image. In particular, the average of the gray levels on the perpendicular line to the skeleton is used. A semi-automatic procedure is used to extract the segments from scanned images. The repository consists of 3.000 images of 6 different segments. Binary-output Support Vector Machine classifiers are used. Two types of cross validation, K-fold and Leave-one-out, are implemented to objectively evaluate the descriptor performance. The results are encouraging. A hit rate of 98% in identity verification is obtained for the 6 segments studied.
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Arabadjis, D., Giannopoulos, F., Papaodysseus, C., Zannos, S., Rousopoulos, P., Panagopoulos, M., Blackwell, C.: New mathematical and algorithmic schemes for pattern classification with application to the identification of writers of important ancient documents. Pattern Recogn. 46(8), 2278–2296 (2013)
Papaodysseus, C., Rousopoulos, P., Giannopoulos, F., Zannos, S., Arabadjis, D., Panagopoulos, M., Kalfa, E., Blackwell, C., Tracy, S.: Identifying the writer of ancient inscriptions and byzantine codices. a novel approach. Comput. Vis. Image Understand. 121, 57–73 (2014)
Hafemann, L.G., Sabourin, R., Oliveira, L.S.: Offline handwritten signature verification—literature review. In: Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA), pp. 1–8 IEEE (2017)
Smekal, Z., Mekyska, J., Rektorova, I., Faundez-Zanuy, M.: Analysis of neurological disorders based on digital processing of speech and handwritten text. In: 2013 International Symposium on Signals, Circuits and Systems (ISSCS), pp. 1–6. IEEE (2013)
Mekyska, J., Faundez-Zanuy, M., Mzourek, Z., Galaz, Z., Smekal, Z., Rosenblum, S.: Identification and rating of developmental dysgraphia by handwriting analysis. IEEE Trans. Hum.-Mach. Syst. 47(2), 235–248 (2017)
Kotsavasiloglou, C., Kostikis, N., Hristu-Varsakelis, D., Arnaoutoglou, M.: Machine learning based classification of simple drawing movements in Parkinson’s disease. Biomed. Signal Process. Control 31, 174–180 (2017)
Crespo, Y., Soriano, M.F., Iglesias-Parro, S., Aznarte, J.I., Ibáñez-Molina, A.J.: Spatial analysis of handwritten texts as a marker of cognitive control. J. Motor Behav. 50(6), 643–652 (2018)
Siddiqi, I., Djeddi, C., Raza, A., Souici-meslati, L.: Automatic analysis of handwriting for gender classification. Pattern Anal. Appl. 18(4), 887–899 (2014). https://doi.org/10.1007/s10044-014-0371-0
Bouadjenek, N., Nemmour, H., Chibani, Y.: Robust soft-biometrics prediction from off-line handwriting analysis. Appl. Soft Comput. 46, 980–990 (2016)
Horster, P.: Communications and Multimedia Security II, Springer, Cham (2016)
Vielhauer, C.: Biometric user authentication for IT security: from fundamentals to handwriting, vol, 18. Springer, Heidelberg (2005)
Lewis, J.: Forensic document examination: Fundamentals and current trends. Elsevier (2014)
Ramos, D., Krish, R.P., Fierrez, J., Meuwly, D.: From biometric scores to forensic likelihood ratios. In: Handbook of Biometrics for Forensic Science, pp. 305–327, Springer, Cham (2017)
Kam, M., Abichandani, P., Hewett, T.: Simulation detection in handwritten documents by forensic document examiners. J. Forensic Sci. 60(4), 936–941 (2015)
Delac, K., Grgic, M.: A survey of biometric recognition methods. In: Proceedings Elmar 2004 46th International Symposium Electronics in Marine, 2004, pp. 184–193. IEEE (2004)
Halder, C., Obaidullah, S.M., Roy, K.: Offline writer identification and verification. A state-of-the-art. In: Information Systems Design and Intelligent Applications, pp. 153–163. Springer, Heidelberg (2016)
Abdi, M.N., Khemakhem, M.: A model-based approach to offline text- independent Arabic writer identification and verification. Pattern Recogn. 48(5), 1890–1903 (2015)
Bensefia, A., Paquet, T.: Writer verification based on a single hand- writing word samples. EURASIP J. Image Video Process. 34(1), 1–9 (2016)
Bertolini, D., Oliveira, L.S., Justino, E., Sabourin, R.: Texture-based descriptors for writer identification and verification. Expert Syst. Appl. 40(6), 2069–2080 (2013)
Okawa, M., Yoshida, K.: Offline writer verification based on forensic expertise: Analyzing multiple characters by combining the shape and advanced pen pressure information. Japanese J. Forensic Sci. Technol. 22(2), 61–75 (2017)
Impedovo, D., Pirlo, G., Russo, M.: Recent advances in offline signature identification. In: 14th International Conference on Frontiers in Handwriting Recognition, pp. 639–642. IEEE (2014)
Aubin, V., Mora, M., Santos, M.: A new descriptor for people recognition by handwritten strokes analysis. In: International Conference on Pattern Recognition Systems (ICPRS-16), vol. 14, no. 6, IET (2016)
Aubin, V., Mora, M., Santos, M.: Off-line writer verification based on simple graphemes. Pattern Recogn. 79, 414–426 (2018)
Aubin, V., Mora, M.: A new descriptor for person identity verification based on handwritten strokes off-line analysis. Expert Syst. Appl. 89, 241–253 (2017)
Zhang, T.Y., Suen, C.Y.: A fast parallel algorithm for thinning digital patterns. Commun. ACM 27(3), 236–239 (1984)
Farias, G., Dormido-Canto, S., Vega, J., Sánchez, J., Duro, N., Dormido, R., Pajares, G.: Searching for patterns in TJ-II time evolution signals. Fus. Eng. Des. 81(15–17), 1993–1997 (2006)
Imdad, A., Bres, S., Eglin, V., Rivero-Moreno, C., Emptoz, H.: Writer identification using steered hermite features and SVM. In: Ninth International Conference on Document Analysis and Recognition (ICDAR 2007), vol. 2, pp. 839–843. IEEE (2007)
Christlein, V., Bernecker, D., Hönig, F., Maier, A., Angelopoulou, E.: Writer identification using GMM supervectors and exemplar-SVMs. Pattern Recogn. 63, 258–267 (2017)
Khan, F.A., Tahir, M.A., Khelifi, F., Bouridane, A., Almotaeryi, R.: Robust off-line text independent writer identification using bagged discrete cosine transform features. Expert Syst. Appl. 71, 404–415 (2017)
Rojas-Thomas, J.C., Mora, M., Santos, M.: Neural networks ensemble for automatic DNA microarray spot classification. Neural Comput. Appl. 31(7), 2311–2327 (2017). https://doi.org/10.1007/s00521-017-3190-6
Chatterjee, I., Ghosh, M., Singh, P.K., Sarkar, R., Nasipuri, M.: A clustering-based feature selection framework for handwritten Indic script classification. Expert Syst. 36(6), e12459 (2019)
López, V., Fernández, A., García, S., Palade, V., Herrera, F.: An insight into classification with imbalanced data: empirical results and current trends on using data intrinsic characteristics. Inf. Sci. 250, 113–141 (2013)
Naranjo, R., Santos, M., Garmendia, L.: A convolution-based distance measure for fuzzy singletons and its application in a pattern recognition problem. Integrated Computer-Aided Engineering, (Preprint), 1–13 (2020)
Parra, B., Vegetti, M., Leone, H.: Advances in the application of ontologies in the area of digital forensic electronic mail. IEEE Latin America Trans. 17(10), 1694–1705 (2019)
Fernandez, C., Pantano, N., Godoy, S., Serrano, E., Scaglia, G.: Parameters optimization applying Monte Carlo methods and evolutionary algorithms. Enforcement to a trajectory tracking controller in non-linear systems. Revista Iberoamericana de Automatica e Informatica Industrial, 16(1), 89–99 (2019)
Rodríguez-Blanco, T., Sarabia, D., De Prada, C.: Real-time optimization using the modifier adaptation methodology. Revista Iberoamericana de Automática e Informática Industrial 15(2), 133–144 (2018)
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Aubin, V., Santos, M., Mora, M. (2021). Off-Line Writer Verification Using Segments of Handwritten Samples and SVM. In: Herrero, Á., Cambra, C., Urda, D., Sedano, J., Quintián, H., Corchado, E. (eds) 13th International Conference on Computational Intelligence in Security for Information Systems (CISIS 2020). CISIS 2019. Advances in Intelligent Systems and Computing, vol 1267. Springer, Cham. https://doi.org/10.1007/978-3-030-57805-3_6
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