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Block Wise 3D Palmprint Recognition Based on Tan and Triggs with BSIF Descriptor


Affiliations
1 Department of Computer Science, Mohamed El Bachir El Ibrahimi University of Bordj Bou Arreridj, Algeria
2 LMSE Laboratory, Mohamed El Bachir El Ibrahimi University of Bordj Bou Arreridj, Algeria
     

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Faced by problems such as lack of robustness from 2D palmprint recognition system which can result to be attacked using a fake palmprint or having the same palmprint as another individual, 3D can present an alternative solution to deal with this problem, hence in this paper we are going to introduce a novel approach based on 3D palmprint recognition system named TT-P-BSIF: first, a preprocessing technique based on Tan and Triggs method was applied on a 3D depth image in order to effectively and efficiently eliminate the effect of low frequency component and at the same time keeping the local statistical properties of the treated image. Then the processed image is divided into a regular number of blocks using two parameters (a and b), after that the Binarized Statistical local features (BSIF) has been applied on each block in order to extract the features vector. These vectors are all combined to produce one larger vector for each processed image. Afterwards nearest neighbor classifier is used to classifier the 3D palmprint images. To examine the proposed method, this latter has been evaluated on a 3D palmprint database that contains 8,000 samples, the obtained results were consistent and promising which proves that the introduced method can massively and effectively improve the recognition results. Therefore, this proposed work using Tan and Triggs method for preprocessing and BSIF for feature extraction was able to generate a recognition rate up to 99.63% and verification rate at 1% up to 100% with EER equals to 0.12%.

Keywords

3D Palmprint, Tan and Triggs, BSIF, Nearest Neighbor Classifier.
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  • J Engel, “Epilepsy Studies-Overview”, Elsevier, 2014.

  • Hae Won Shin, Valerie Jewells, Eldad Hadar, Tiffany Fisher and Albert Hinn, “Review of Epilepsy-Etiology, Diagnostic Evaluation and Treatment”, International Journal of Neurorehabilitation, Vol. 1, No. 3, pp. 1-6, 2014.

  • Suruchi Sharma and Vaishali Dixit, “Epilepsy-A Comprehensive Review”, International Journal of Pharma Research and Review, Vol. 2, No. 12, pp. 61-80, 2013.

  • Emily L. Johnson, “Seizures and Epilepsy”, Medical Clinics, Vol. 103, No. 2, pp. 309-324, 2019.

  • Roland D Thijs and Rainer Surges, “Epilepsy in Adults”, Lancet, Vol. 24, No. 16, pp. 689-701, 2019.

  • Hirak Kumar Mukhopadhyay, Chandi Charan Kandar, Sanjay Kumar Das, Lakshmikanta Ghosh and Bijan Kumar Gupta, “Epilepsy and its Management: A Review”, Journal of PharmaSci Tech, Vol. 1, No. 2, pp. 20-26, 2012.

  • D. Zhang, G. Lu, W. Li, L. Zhang and N.Luo, “Palmprint Recognition using 3-D Information”, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), Vol. 39, No. 5, pp. 505-519, 2009.

  • M.E. Tipping, “The Relevance Vector Machine”, Proceedings of International Conference on Neural Information Processing Systems, pp. 652-658, 2000.

  • M.E. Tipping, “Sparse Bayesian Learning and the Relevance Vector Machine”, Journal of Machine Learning Research, Vol. 1, No. 2, pp. 211-244, 2001.

  • P. Coutin Churchman, Y. Anez and M. Uzcategui, “Quantitative Spectral Analysis of EEG in Psychiatry Revisited: Drawing Signs Out of Numbers in a Clinical Setting”, Clin Neurophysiology, Vol. 114, No. 12, pp. 2294-2306, 2003.

  • D. Zhang, V. Kanhangad, N. Luo and A. Kumar, “Robust Palmprint Verification using 2D and 3D Features”, Pattern Recognition, Vol. 43, No. 1, pp. 358-368, 2010.

  • S. Walton, O. Hassan, K. Morgan and M.R. Brown, “A Review of the Development and Applications of the Cuckoo Search Algorithm”, Swarm Intelligence and Bio-Inspired Computation Theory and Applications, Vol. 25, No. 2, pp. 257-271, 2013.

  • M. Dorigo, “Optimization, Learning and Natural Algorithms”, PhD Dissertation, Department of Computer Science and Engineering, Dipartimento di Elettronica, Politecnico di Milano, pp. 1-145, 1992.

  • M. Dorigo and Maniezzo V, Colorni, “An Ant System: Optimization by a Colony of Cooperating Agents”, IEEE Transactions on Systems, Man, and Cybernetics Part B: Cybernetics, Vol. 26, No. 1, pp. 29-41, 1996.

  • R. Musa, J.P. Arnaout and H. Jung, “Ant Colony Optimization Algorithm to Solve for the Transportation Problem of Cross-Docking Network”, Computers and Industrial Engineering, Vol. 59, No. 1, pp. 85-92, 2010.

  • D. Meier, I. Tullumi, Y. Stauffer, R. Dornberger and T. Hanne, “A Novel Backup Path Planning Approach with ACO”, Proceedings of International Conference on Computer and Business Intelligence, pp. 50-56, 2017.

  • C. Park, “Epileptic Seizure Detection for Multi-Channel EEG with Deep Convolutional Neural Network”, Proceedings of International Conference on Electronics, Information, and Communication, pp. 1-8, 2018.

  • C.M. Bishop, “Pattern Recognition and Machine Learning”, Springer, 2006.

  • D.W. Hosmer, S. Lemeshow and R.X. Sturdivant, “Applied Logistic Regression”, Wiley, 2013.

  • A. Alkan, E. Koklukaya and A. Subasi, “Automatic Seizure Detection in EEG using Logistic Regression and Artificial Neural Network”, Journal of Neuroscience Methods, Vol. 148, pp. 167-176, 2005.

  • A. Subasi, A. Alkana, E. Koklukayab and M.K. Kiymik, “Analysis of Epileptic Seizure Detection Methods based on Parameter Estimation, Power Spectrum Density and Morlet Wavelet Transform”, Neural Networks, Vol. 18, pp. 985-997, 2005.

  • X.S. Yang and S. Deb, “Cuckoo Search via Levy Flights”, Proceedings of the World Congress on Nature and Biologically Inspired Computing, pp. 210-214, 2009.

  • G. Rodrguez, Generalized Linear Models”, Available at: https://data.princeton.edu/wws509/notes/, Accessed at 2018.

  • K. Deepa and S. Suganya, “A Novel Approach of Segmentation of Cytomegalovirus Image using K-Means Clustering and Discrete Wavelet Transform”, Proceedings of International Conference on Emerging Trends in Information Technology and Engineering, pp. 341-345, 2020.


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  • Block Wise 3D Palmprint Recognition Based on Tan and Triggs with BSIF Descriptor

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Authors

Nour Elhouda Chalabi
Department of Computer Science, Mohamed El Bachir El Ibrahimi University of Bordj Bou Arreridj, Algeria
Abdelouahab Attia
LMSE Laboratory, Mohamed El Bachir El Ibrahimi University of Bordj Bou Arreridj, Algeria
Abderraouf Bouziane
Department of Computer Science, Mohamed El Bachir El Ibrahimi University of Bordj Bou Arreridj, Algeria

Abstract


Faced by problems such as lack of robustness from 2D palmprint recognition system which can result to be attacked using a fake palmprint or having the same palmprint as another individual, 3D can present an alternative solution to deal with this problem, hence in this paper we are going to introduce a novel approach based on 3D palmprint recognition system named TT-P-BSIF: first, a preprocessing technique based on Tan and Triggs method was applied on a 3D depth image in order to effectively and efficiently eliminate the effect of low frequency component and at the same time keeping the local statistical properties of the treated image. Then the processed image is divided into a regular number of blocks using two parameters (a and b), after that the Binarized Statistical local features (BSIF) has been applied on each block in order to extract the features vector. These vectors are all combined to produce one larger vector for each processed image. Afterwards nearest neighbor classifier is used to classifier the 3D palmprint images. To examine the proposed method, this latter has been evaluated on a 3D palmprint database that contains 8,000 samples, the obtained results were consistent and promising which proves that the introduced method can massively and effectively improve the recognition results. Therefore, this proposed work using Tan and Triggs method for preprocessing and BSIF for feature extraction was able to generate a recognition rate up to 99.63% and verification rate at 1% up to 100% with EER equals to 0.12%.

Keywords


3D Palmprint, Tan and Triggs, BSIF, Nearest Neighbor Classifier.

References