Offline Signature Biometric Verification with Length Normalization using Convolution Neural Network

Main Article Content

Zahraa Mazin Alkattan
Ghada Mohammad Tahir Aldabagh


Offline handwritten signature is a type of behavioral biometric-based on an image. Its problem is the accuracy of the verification because once an individual signs, he/she seldom signs the same signature. This is referred to as intra-user variability. This research aims to improve the recognition accuracy of the offline signature. The proposed method is presented by using both signature length normalization and histogram orientation gradient (HOG) for the reason of accuracy improving. In terms of verification, a deep-learning technique using a convolution neural network (CNN) is exploited for building the reference model for a future prediction. Experiments are conducted by utilizing 4,000 genuine as well as 2,000 skilled forged signature samples collected from 200 individuals. This database is publicly distributed under the name of SIGMA for Malaysian individuals. The experimental results are reported as both error forms, namely False Accept Rate (FAR) and False Reject Rate (FRR), which achieved up to 4.15% and 1.65% respectively. The overall successful accuracy is up to 97.1%. A comparison is also made that the proposed methodology outperforms the state-of-the-art works that are using the same SIGMA database.


Download data is not yet available.

Article Details

How to Cite
Alkattan ZM, Aldabagh GMT. Offline Signature Biometric Verification with Length Normalization using Convolution Neural Network. Baghdad Sci.J [Internet]. 2022 Oct. 1 [cited 2022 Nov. 30];19(5):1100. Available from:


Singh M, Singh R, Ross A. A comprehensive overview of biometric fusion. Inf. Fusion. 2019;52:187-205.

Elhoseny M, Elkhateb A, Sahlol A, Hassanien AE. Multimodal biometric personal identification and verification. Adv. in Soft Comput. Machine Learning in Image Processing: Springer; 2018. p. 249-76.

Jain AK, Kumar A. Biometrics of next generation: An overview. Second generation biometrics. 2010;12(1):2-3.

Vaijayanthimala J, Padma T. Multi-modal biometric authentication system based on face and signature using legion feature estimation technique. Multimed Tools Appl. . 2020;79(5):4149-68.

Bibi K, Naz S, Rehman A. Biometric signature authentication using machine learning techniques: Current trends, challenges and opportunities. Multimed Tools Appl. . 2020;79(1):289-340.


Faundez-Zanuy M, Fierrez J, Ferrer MA, Diaz M, Tolosana R, Plamondon R. Handwriting biometrics: Applications and future trends in e-security and e-health. Cognit. Comput. . 2020;12(5):940-53.

Malallah FL, Ahmad SMS, Adnan WAW, Arigbabu OA, Iranmanesh V, Yussof S. Online handwritten signature recognition by length normalization using up-sampling and down-sampling. International Journal of Cyber-Security and Digital Forensics (IJCSDF). 2015;4(1):302-13.

Iranmanesh V, Ahmad SMS, Adnan WAW, Yussof S, Arigbabu OA, Malallah FL. Online handwritten signature verification using neural network classifier based on principal component analysis. Sci. World J. . 2014;2014.

Abbas NH, Yasen KN, Faraj K, Razak LFA, Malallah FL. Offline handwritten signature recognition using histogram orientation gradient and support vector machine. J. Theor. Appl. Inf. Technol. . 2018;96(8):2075-84.

Sriwathsan W, Ramanan M, Weerasinghe A. Offline handwritten signature recognition based on SIFT and SURF features using SVMs. Asian Res. J. of Mathematics. 2020:84-91.

Maruyama TM, Oliveira LS, Britto AS, Sabourin R. Intrapersonal parameter optimization for offline handwritten signature augmentation. IEEE Transactions on Information Forensics and Security. 2020;16:1335-50.

Yapıcı MM, Tekerek A, Topaloğlu N. Deep learning-based data augmentation method and signature verification system for offline handwritten signature. Pattern Anal. Appl. . 2020:1-15.

Yin L, Yang R, Gabbouj M, Neuvo Y. Weighted median filters: a tutorial. IEEE Trans. circuits syst II.: analog and digital signal processing. 1996;43(3):157-92.

Frajka T, Zeger K. Downsampling dependent upsampling of images. Signal Processing: Image Commun. . 2004;19(3):257-65.

Dalal N, Triggs B, editors. Histograms of oriented gradients for human detection. 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR'05); 2005: Ieee.

O'Shea K, Nash R. An introduction to convolutional neural networks. arXiv preprint arXiv:151108458. 2015 Nov 26.

Hassan NF, Abdulrazzaq HI. Pose invariant palm vein identification system using convolutional neural network. Baghdad Sci. J.. 2018;15(4).

Asroni A, Ku-Mahamud KR, Damarjati C, Slamat HB. Arabic Speech Classification Method Based on Padding and Deep Learning Neural Network. Baghdad Sci. J.. 2021;18(2 (Suppl.)):0925-.

Ahmad SMS, Shakil A, Ahmad AR, Agil M, Balbed M, Anwar RM, editors. SIGMA-A Malaysian signatures’ database. 2008 IEEE/ACS International Conference on Computer Systems and Applications; 2008: IEEE.

Jain AK, Griess FD, Connell SD. On-line signature verification. Pattern recognition. 2002;35(12):2963-72.

Fabris F, De Magalhães JP, Freitas AA. A review of supervised machine learning applied to ageing research. Biogerontology. 2017;18(2):171-88.

Kingma DP, Ba J. Adam: A method for stochastic optimization. arXiv preprint arXiv:14126980. 2014.

Murphy KP. Machine learning: a probabilistic perspective: MIT press; 2012.