Abstract
In this paper, an enhanced quantum-based neural network learning algorithm (EQNN-S) which constructs a neural network architecture using the quantum computing concept is proposed for signature verification. The quantum computing concept is used to decide the connection weights and threshold of neurons. A boundary threshold parameter is introduced to optimally determine the neuron threshold. This parameter uses min, max function to decide threshold, which assists efficient learning. A manually prepared signature dataset is used to test the performance of the proposed algorithm. To uniquely identify the signature, several novel features are selected such as the number of loops present in the signature, the boundary calculation, the number of vertical and horizontal dense patches, and the angle measurement. A total of 45 features are extracted from each signature. The performance of the proposed algorithm is evaluated by rigorous training and testing with these signatures using partitions of 60–40 and 70–30%, and a tenfold cross-validation. To compare the results derived from the proposed quantum neural network, the same dataset is tested on support vector machine, multilayer perceptron, back propagation neural network, and Naive Bayes. The performance of the proposed algorithm is found better when compared with the above methods, and the results verify the effectiveness of the proposed algorithm.
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References
Bajaj R, Chaudhury S (1997) Signature verification using multiple neural classifiers. Pattern Recognit 30(1):1–7
Bhattacharya I, Ghosh P, Biswas S (2013) Offline signature verification using pixel matching technique. Procedia Technol 30:970–977
Buryne PD, Forre R (1986) Signature verification with elastic image matching. In: International Carnahan conference on security technology Gothenburg, Sweden, August. IEEE, pp 12–14
Castellani M (2017) Competitive co-evolution of multi-layer perceptron classifiers. Soft Comput: 1–16. https://doi.org/10.1007/s00500-017-2587-6
Chan L-H, Salleh S-H, Ting C-M (2009) PCA, LDA and neural network for face identification. In: 4th IEEE conference on industrial electronics and applications, Xian, China, IEEE, pp 1256–1259
Dimauro G, Impedovo S, Pirlo G, Salzo A (1997) A multi-expert signature verification system for bankcheck processing. IJPRAI 11(5):827–844
Ferrer M, Vargas J, Morales A, Ordonez A (2012) Robustness of offline signature verification based on gray level features. IEEE Trans Inf Forensics Secur 7(3):966–977
Gupta AK, Singh YP (2011) Analysis of bidirectional associative memory of neural network method in the string recognition. In: International conference on computational intelligence and communication networks (CICN), Gwalior, India, 2011. IEEE, pp 172–176
Han K-H, Kim J-H (2002) Quantum-inspired evolutionary algorithm for a class of combinatorial optimization. IEEE Trans Evol Comput 6(6):580–593
Kruthi C, Shet DC (2014) Offline signature verification using support vector machine. In: Fifth international conference on signal and image processing (ICSIP), Bangalore, India, 2014. IEEE, pp 3–8
Kuzuno H, Tonami S (2015) Detection of sensitive information leakage in Android applications using signature generation. Int J Space Based Situated Comput 5(1):53–62
Lu T-C, Yu G-R, Juang J-C (2013) Quantum-based algorithm for optimizing artificial neural networks. IEEE Trans Neural Netw Learn Syst 24(8):1266–1278
Nakamura S, Duolikun D, Enokido T, Takizawa M (2016) A read-write abortion protocol to prevent illegal information flow in role-based access control systems. Int J Space Based Situated Comput 6(1):43–53
Pansare A, Bhatia S (2012) Handwritten signature verification using neural network. Int J Appl Inf Syst 1(2):44–49
Patel OP, Tiwari A (2014) Quantum inspired binary neural network algorithm. In: International conference on information technology (ICIT), Bhubaneswar, India, 2014. IEEE, pp 270–274
Qi Y, Hunt BR (1994) Signature verification using global and grid features. Pattern Recognit 27(12):1621–1629
Qiu L, Sun X, Xu J (2017) Categorical quantum cryptography for access control in cloud computing. Soft Comput: 1–8
Ren Y, Wang H, Du J, Ma L (2016) Code-based authentication with designated verifier. Int J Grid Util Comput 7(1):61–67
Sanmorino A, Yazid S (2012) A survey for handwritten signature verification. In: 2nd International conference on uncertainty reasoning and knowledge engineering (URKE), Jakarta, Indonesia, 2012. IEEE, pp 54–57
Sarikaya R, Hinton GE, Deoras A (2014) Application of deep belief networks for natural language understanding. IEEE/ACM Trans Audio Speech Lang Process 22(4):778–784
Sartakhti JS, Afrabandpey H, Saraee M (2017) Simulated annealing least squares twin support vector machine (SA-LSTSVM) for pattern classification. Soft Comput 21(15):4361–4373
Shanker AP, Rajagopalan A (2007) Off-line signature verification using DTW. Pattern Recognit Lett 28(12):1407–1414
Sun X, Tian H, Wang Y, Ren Y (2014) Toward quantum-resistant strong designated verifier signature. Int J Grid Util Comput 5(2):80–86
Wang T, Wang Y (2010) Pattern classification with ordered features using mrmr and neural networks. In: International conference on information, networking and automation, ICINA 2010, Kunming, China, 2010, pp 2128–2131
Wang Y, Ma J, Lu X, Lu D, Zhang L (2016) Efficiency optimisation signature scheme for time-critical multicast data origin authentication. Int J Grid Util Comput 7(1):1–11
Wilkinson TS, Pender DA, Goodman JW (1991) Use of synthetic discriminant functions for handwritten-signature verification. Appl Opt 30(23):3345–3353
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Patel, O.P., Tiwari, A., Chaudhary, R. et al. Enhanced quantum-based neural network learning and its application to signature verification. Soft Comput 23, 3067–3080 (2019). https://doi.org/10.1007/s00500-017-2954-3
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DOI: https://doi.org/10.1007/s00500-017-2954-3