• Aini Najwa Azmi Department of Computer Science, Faculty of Computing, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia
  • Dewi Nasien Department of Computer Science, Faculty of Computing, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia
  • Azurah Abu Samah Department of Computer Science, Faculty of Computing, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia



Offline signature verification system, feature extraction, Freeman Chain Code (FCC), global feature, verification


Over recent years, there has been an explosive growth of interest in the pattern recognition. For example, handwritten signature is one of human biometric that can be used in many areas in terms of access control and security. However, handwritten signature is not a uniform characteristic such as fingerprint, iris or vein. It may change to several factors; mood, environment and age. Signature Verification System (SVS) is a part of pattern recognition that can be a solution for such situation. The system can be decomposed into three stages: data acquisition and preprocessing, feature extraction and verification. This paper presents techniques for SVS that uses Freeman chain code (FCC) as data representation. In the first part of feature extraction stage, the FCC was extracted by using boundary-based style on the largest contiguous part of the signature images. The extracted FCC was divided into four, eight or sixteen equal parts. In the second part of feature extraction, six global features were calculated. Finally, verification utilized k-Nearest Neighbour (k-NN) to test the performance. MCYT bimodal database was used in every stage in the system. Based on our systems, the best result achieved was False Rejection Rate (FRR) 14.67%, False Acceptance Rate (FAR) 15.83% and Equal Error Rate (EER) 0.43% with shortest computation, 7.53 seconds and 47 numbers of features.


Jain, A. K., Duin, R. P., and Mao, J. 2000. Statistical Pattern Recognition: A Review. IEEE Transactions on Pattern Analysis and Machine Intelligence. 22(1): 4-37

Basu, J. K., Bhattacharyya D., and Kim, T. 2010. Use of Artificial Neural Network in Pattern Recognition. Journal of Software Engineering and Its Application. 4(2): 23-34.

Fierrez, J., Ortega-Garcia, J., Ramos, D., and Gonzalez-Rodriguez, J. 2007. HMM-Based On-Line Signature Verification: Feature Extraction and Signature Modeling. Pattern Recognition Letters. 28(16): 2325-2334.

Alonso-Fernandez, F., Fairhurst, M. C., Fierrez, J., and Ortega-Garcia, J. 2007. Automatic Measures for Predicting Performance in Off-line Signature. IEEE Texas International Conference Image Processing. 1-369.

Serdouk, Y., Nemmour, H., and Chibani, Y. 2016. New Off-line Handwritten Signature Verification Method based on Artificial Immune Recognition System. Expert Systems with Applications. Elsevier.

Djeddi, C., Siddiqi, I., Al-Maadeed, S., Souici-Meslati, L., Gattal, A., and Ennaji, A. 2015. Signature Verification for Offline Skilled Forgeries Using Textural Features. In 2015 11th International Conference on Signal-Image Technology & Internet-Based Systems (SITI). IEEE. 76-80.

Vargas, V., Ferrer, M. A., Travieso, C.M., and Alonso, J. B. 2011. Off-line Signature Verification Based on Grey Level Information using Texture Features, Pattern Recognition. 44(2): 375-385.

Impedovo, D., and Pirlo, G. 2011. Stability Analysis of Static Signatures for Automatic Signature Verification. In G. Maino & G. Foresti (Eds.), Image Analysis and Processing – ICIAP 2011. Springer Berlin Heidelberg. 241-247.

Nanni, L., Lumini, A., and Brahnam, S. 2010. Local Binary Patterns Variants as Texture Descriptors for Medical Image analysis. Artificial intelligence in Medicine. 49(2): 117-125.

Neamah, K., Mohamad, D., Saba, T., and Rehman, A. 2014. Discriminative Features Mining For Offline Handwritten Signature Verification. Journal of 3D Research. 5(1): 1-6

Impedovo, D., Pirlo, G., and Plamondon, R. 2012. Handwritten Signature Verification: New Advancements

And Open Issues. In Conference on Frontiers in Handwriting Recognition (ICFHR), 2012 International. IEEE. 367-372.

Enqi Z., Jinxu, G., Jianbin Z., Chan M., Linjuan W. 2009. Online Handwritten Signature Verification based on Two Levels Back Propagation Neural Network. Chengdu International Symposium on Intelligent Ubiquitous Computing and Education. 202-205

Ferrer, M., Vargas, J., Morales, A., and Ordóñez, A. 2012. Robustness of Offline Signature Verification Based on Gray Level Features. IEEE Journal of Information Forensics and Security. 7(3): 966-977.

Freeman, H. 1961. Techniques for the Digital Computer Analysis of Chain Encoded Arbitrary Plane Curves. Paper presented at the Electron.

Azmi, A. N., and Nasien, D. 2014. Freeman Chain Code (FCC) Representation in Signature Fraud Detection Based On Nearest Neighbour and Artificial Neural Network (ANN) Classifiers. International Journal of Image Processing (IJIP). 8(6): 434.

Cheriet M., Kharma N., Liu CL., Suen C. 2007. Character Recognition Systems: A Guide for Students and Practitioners. John Wiley & Sons, Canada

Pal S., Alireza A., Pal U., Blumenstein M. 2011. Offline Signature Identification using Background and Foreground Information. Adelaide International Conference on Digital Image Computing Techniques and Applications. 672-677

Ortega-Garcia J., et al 2003. MCYT Baseline Corpus: A Bimodal Biometric Database. IEEE Proceedings Vision, Image and Signal Processing. 150(6): 395-401.

Akram M., Qasim R., and Amin M. A. 2012. A Comparative Study Of Signature Recognition Problem Using Statistical Signatures And Artificial Neural Networks. Dhaka International Conference on Informatics, Electronics & Vision. 925-929,

Munich, M. E., and Perona, P. 2003. Visual identification by signature tracking. Journal of Pattern Analysis and Machine Intelligence. 25(2): 200-217.

Migual A. F., Jesus B. A., and Carlos M. T. 2005. Off-line Geometric Parameters for Automatic Signature Verification Using Fixed-Point Arithmetic. Journal of Pattern Analysis and Machine Intelligence. 27(6): 993-997

Štruc V., Pavešic, N. 2010. The Complete Gabor-Fisher Classifier for Robust Face Recognition. EURASIP Advances in Signal Processing. 1-13

Prakash, H. N. and Guru, D. S. 2010. Offline Signature Verification - An Approach Based on Score Level Fusion. Journal of Computer Applications. 1(18): 0975-8887

Vargas, J. F., Ferrer, M. A., Travieso, C. M., and Alonso, J. B. 2011. Off-line Signature Verification Based on Grey Level information using Texture Features. Journal of Pattern Recognition. 44(2): 75-385

Prakash, H. N., and Guru, D. S. 2009. Relative Orientations of Geometric Centroids for Off-Line Signature Verification. Proceedings of the 2009 IEEE 7th International Conference Advances in Pattern Recognition. February 4-6. Kolkata. 201-204.




How to Cite