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Bouziane, Abderraouf
- Multimodal Finger Dorsal Knuckle Major and Minor Print Recognition System based on Pcanet Deep Learning
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Authors
Affiliations
1 Department of Computer Science, Mohamed El Bachir El Ibrahimi University of Bordj Bou Arreridj, DZ
2 LMSE Laboratory, Mohamed El Bachir El Ibrahimi University, DZ
1 Department of Computer Science, Mohamed El Bachir El Ibrahimi University of Bordj Bou Arreridj, DZ
2 LMSE Laboratory, Mohamed El Bachir El Ibrahimi University, DZ
Source
ICTACT Journal on Image and Video Processing, Vol 10, No 3 (2020), Pagination: 2153-2158Abstract
Hand-based recognition systems with different traits are widely used techniques and are trustworthy ones. We can find it in different real life fields such as banks and industries due to its stability, reliability, acceptability, and the wide range features. In this paper, we present a finger dorsal knuckle print multimodal recognition system, where we use PCAnet (principal component analysis network) deep learning to extract the features from both Major and Minor finger dorsal knuckles to allow a deeper insight into the exploited trait. Then SVM is used for the matching stage of the two modalities, followed by a score level fusion method to combine the scores using different rules. In order to establish the effectiveness of the proposed system, several experiments in the course of this work have been done on the finger knuckle images of the publicly available database PolyUKV1. The results show that the proposed method has better results in comparison with a unimodal system.Keywords
Finger Knuckle Print, Major, Minor, PCAnet, Score Level Fusion, SVM.References
- L. Zhang, L. Zhang and D. Zhang, “Finger-Knuckle-Print: A New Biometric Identifier”, Proceedings of IEEE International Conference on Image Processing, pp. 1981-1984, 2009.
- N. Duta, “A Survey of Biometric Technology based on Hand Shape”, Pattern Recognition, Vol. 42, No. 1, pp. 2797-2806, 2009.
- R. Cappelli, M. Ferrara and D. Maltoni, “Minutia Cylinder-Code: A New Representation and Matching Technique for Fingerprint Recognition”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 32, No. 12, pp. 2128-2141, 2010.
- G. Jaswal, A. Kaul and R. Nath, “Knuckle Print Biometrics and Fusion Schemes-Overview, Challenges, and Solutions”, ACM Computing Survey, Vol. 49, No. 2, pp. 1-34, 2016.
- A. Kumar, “Can We Use Minor Finger Knuckle Images to Identify Humans?”, Proceedings of IEEE 5th International Conference on Biometrics: Theory, Applications and Systems, pp. 55-60, 2012.
- D.L. Woodard and P.J. Flynn, “Finger Surface as a Biometric Identifier”, Computer Vision and Image Understanding, Vol. 100, pp. 357-384, 2005.
- C.K. Sahu and Y. Rathore, “A Survey Paper on Finger Knuckle Print Recognition Algorithm”, International Journal of Research in Computer Applications and Robotics, Vol. 5, No. 6, pp. 13-21, 2017.
- A. Kumar and C. Ravikanth, “Personal Authentication using Finger Knuckle Surface”, IEEE Transactions on Information Forensics and Security, Vol. 4, No. 1, pp. 98-109, 2009.
- M. Ferrer, C. Travieso and J. Alonso, “Using Hand Knuckle Texture for Biometric Identifications”, IEEE Aerospace and Electronic Systems Magazine, Vol. 21, No. 6, pp. 23-27, 2006.
- B. Zeinali, A. Ayatollah and M. Kakooei, “A Novel Method of Applying Directional Filter Bank (DFB) for Finger-Knuckle-Print (FKP) Recognition”, Proceedings of Iranian Conference on Electrical Engineering, pp. 500-504, 2014.
- M. Chaa, N.E. Boukezzoula and A. Meraoumia, “Features-Level Fusion of Reflectance and Illumination Images in Finger-Knuckle-Print Identification System”, International Journal of Artificial Intelligence Tools, Vol. 27, No. 3, p. 1850-1857, 2018.
- Lin. Zhang, Lei. Zhang, David Zhang and Zhenhua Guo, “Phase Congruency Induced Local Features for Finger-Knuckle-Print Recognition”, Pattern Recognition, Vol. 45, No. 7, pp. 2522-2531, 2012.
- L. Zichao, K. Wang and W. Zuo, “Finger-Knuckle-Print Recognition using Local Orientation Feature based on Steerable Filter”, Proceedings of IEEE International Conference on Intelligent Computing, pp. 224-230, 2012.
- G. Jaswal and A. Kaul, “Palmprint and Finger Knuckle Based Person Authentication with Random Forest via Kernel-2DPCA”, Proceedings of International Conference on Pattern Recognition and Machine Intelligence, pp. 233-240, 2017.
- A. Elmahmudi and H. Ugail, “Individual Recognition System using Deep network based on Face Regions”, International Journal of Applied Mathematics, Electronics and Computers, Vol. 6, No. 3, pp. 27-32, 2018.
- T.H. Chan, K. Jia, S. Gao, J. Lu, Z. Zeng and Y. Ma, “PCANet: A Simple Deep Learning Baseline for Image Classification?”, IEEE Transactions on Image Processing, Vol. 24, No. 12, pp. 5017-5032, 2015.
- C. Cortes and V. Vapnik, “Support-Vector Networks”, Machine Learning, Vol. 20, No. 3, pp. 273-297, 1995.
- M. Chaa, N.E. Boukezzoula and A. Abdelouahab, “Score-Level Fusion of Two-Dimensional and Three-Dimensional Palmprint for Personal Recognition Systems”, Journal of Electronic Imaging, Vol. 26, No. 1, pp. 1-15, 2017.
- A. Kumar, “Importance of Being Unique from Finger Dorsal Patterns: Exploring Minor Finger Knuckle Patterns in Verifying Human Identities”, IEEE Transactions on Information Forensics and Security, Vol. 9, No. 8, pp. 1288-1298, 2014.
- A. Kumar and Y. Zhou, “Human Identification using Finger Images”, IEEE Transactions on Image Processing, Vol. 21, No. 4, pp. 2228-2244, 2011.
- A. Attia, A. Moussaoui, M. Chaa and Y. Chahir, “Finger-Knuckle-Print Recognition System based on Features-Level Fusion of Real and Imaginary Images”, ICTACT Journal on Image and Video Processing, Vol. 8, No. 4, pp. 1793-1799, 2018.
- A. Attia, M. Chaa, Z. Akhtar, and Y. Chahir, “Finger Kunckcle Patterns based Person Recognition Via bank of Multi-Scale Binarized Statistical Texture Features”, Evolving Systems, Vol. 9, No. 1, pp. 1-11, 2018.
- Arabic Handwritten Characters Recognition Via Multi-Scale Hog Features and Multi-Layer Deep Rule-Based Classification
Abstract Views :152 |
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Authors
Affiliations
1 Department of Computer Science, Mohamed El Bachir El Ibrahimi University of Bordj Bou Arreridj, DZ
2 Department of Computer Science, University of Memphis, US
3 LMSE Laboratory, Mohamed El Bachir El Ibrahimi University Bordj Bou Arreridj, DZ
1 Department of Computer Science, Mohamed El Bachir El Ibrahimi University of Bordj Bou Arreridj, DZ
2 Department of Computer Science, University of Memphis, US
3 LMSE Laboratory, Mohamed El Bachir El Ibrahimi University Bordj Bou Arreridj, DZ
Source
ICTACT Journal on Image and Video Processing, Vol 10, No 4 (2020), Pagination: 2195-2200Abstract
Optical character recognition systems for handwritten Arabic language still face challenges, owing to high level of ambiguity, complexity and tremendous variations in human writing styles. In this paper, we propose a new and effective Arabic handwritten characters recognition framework using multi-scale histogram oriented gradient (HOG) features and the deep rule-based classifier (DRB). In the feature extraction stage, the proposed framework combines multi-scale HOG features, and then the DRB is applied on comprehensive HOG features to obtain the final classification label/class. This study involves experimental analyses that were conducted on the publicly available cursive Arabic Handwritten Characters Database (AHCD) containing 16800 characters. Experimental results demonstrate the efficacy of the proposed recognition system compared to the existing state-of-the-art-systems.Keywords
Arabic Character Recognition, Writing, DRB Classifier, HOG, AHCD.References
- Ahmed El Sawy, M. Loey and Hazem E.L. Bakry, “Arabic Handwritten Characters Recognition using Convolutional Neural Network”, WSEAS Transactions on Computer Research, Vol. 5, pp. 11-19, 2017.
- A. Lawgali, “Arabic Character Recognition: A Survey”, International Journal of Signal Processing, Image Processing and Pattern Recognition, Vol. 8, No 2, pp. 401-426, 2015.
- A. Cheung, M. Bennamoun and N.W. Bergmann, “An Arabic Optical Character Recognition System using Recognition-Based Segmentation”, Pattern Recognition, Vol. 34, No. 2, pp. 215-233, 2001.
- A. Goyal, K. Khandelwal and P. Keshri, “Optical Character Recognition for Handwritten Hindi”, CS229 Notes-Machine Learning, Stanford University, pp. 1-5, 2010.
- P.P. Angelov and G.U. Xiaowei, “Deep Rule-Based Classifier with Human-Level Performance and Characteristics”, Information Sciences, Vol. 463, pp. 196-213, 2018.
- M. Shatnawi and S. Abdallah, “Improving Handwritten Arabic Character Recognition by Modeling Human Handwriting Distortions”, ACM Transactions on Asian and Low-Resource Language Information Processing, Vol. 15, No. 1, pp. 1-12, 2015.
- M. Elzobi, A. Al Hamadi, Z. Al Aghbari, L. Dings and A. Saeed, “Gabor Wavelet Recognition Approach for Off-Line Handwritten Arabic using Explicit Segmentation”, Image Processing and Communications Challenges, Springer, pp. 245-254, 2014.
- M. Elzobi, Moftah, A. Al Hamadi and Z. Al Aghbari, “A Database for Handwritten Arabic and An Optimized Topological Segmentation Approach”, International Journal on Document Analysis and Recognition, Vol. 16, No. 3, pp. 295-308, 2013.
- M. Pechwitz, S.S. Maddouri, V. Margner, N. Ellouze and H. Amiri, “IFN/ENIT-Database of Handwritten Arabic 575 Words”, Proceedings of International Symposium on Writing and Documents, pp. 127-136, 2002.
- A. Sahlol and C. Suen, “A Novel Method for the Recognition of Isolated Handwritten Arabic Characters”, Proceedings of International Conference on Computer Vision and Pattern Recognition, pp. 1-13, 2014.
- A. Maqqor, A. Halli, K. Satori and H. Tairi, “Off-Line Recognition Handwriting Combination of Mutiple Classifiers”, Proceedings of International Conference on Information Science and Technology, pp. 1-12, 2014.
- Yasser M. Alginahi, “Arabic Character Segmentation: A Survey”, International Journal on Document Analysis and Recognition, Vol. 16, No. 2, pp. 105-126, 2013.
- K. Jumari and M.A. Ali, “A Comparative Evaluation of Selected Off-Line Arabic Handwritten Character Recognition Systems: A Survey”, Jurnal Teknologi, Vol. 36, No. 1, pp. 1-18, 2012.
- Ramzi A. Haraty, “Arabic Text Recognition”, International Arab Journal of Information Technology, Vol. 1, No. 2, pp. 156-163, 2004.
- S. Khorashadizadeh and A. Latif, “Arabic/Farsi Handwritten Digit Recognition using Histogram of Oriented Gradient and Chain Code Histogram”, International Arab Journal of Information Technology, Vol. 13, No. 4, pp. 1-13, 2016.
- Navneet Dalal and Bill Triggs, “Histograms of Oriented Gradients for Human Detection”, Proceedings of International Conference on Computer Vision and Pattern Recognition, pp. 886-893, 2005.