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Attia, Abdelouahab
- Finger-Knuckle-Print Recognition System Based on Features-level Fusion of Real and Imaginary Images
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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, Ferhat Abbas University, DZ
3 Department of New Technologies of Information and Communication, Ouargla University, DZ
4 Department of Computer Science, University of Caen Lower, FR
1 Department of Computer Science, Mohamed El Bachir El Ibrahimi University of Bordj Bou, Arreridj, DZ
2 Department of Computer Science, Ferhat Abbas University, DZ
3 Department of New Technologies of Information and Communication, Ouargla University, DZ
4 Department of Computer Science, University of Caen Lower, FR
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ICTACT Journal on Image and Video Processing, Vol 8, No 4 (2018), Pagination: 1793-1799Abstract
In this paper, a new method based on Log Gabor- TPLBP (LGTPLBP) has been proposed. However the Three Patch Local Binary Patterns (TPLBP) technique used in face recognition has been applied in Finger-Knuckle-Print (FKP) recognition. The 1D- Log Gabor filter has been used to extract the real and the imaginary images from each of the Region of Interest (ROI) of FKP images. Then the TPLBP descriptor on both images has been applied to extract the feature vectors of the real image and the imaginary image respectively. These feature vectors have been jointed to form a large feature vector for each image FKP. After that, the obtained feature vectors of all images are processed directly with a dimensionality reduction algorithm, using linear discriminant analysis (LDA). Finally, the cosine Mahalanobis distance (MAH) has been used for matching stage. To evaluate the effectiveness of the proposed system several experiments have been carried out. The Hong Kong Polytechnic University (PolyU) FKP database has been used during all of the tests. Experimental results show that the introduced system achieves better results than other state-of-the-art systems for both verification and identification.Keywords
Biometric Systems, Three Patch Local Binary Patterns, 1D Log Gabor Filter, Finger Knuckle Print.References
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- S.Z. Shariatmadar and K. Faez, “Finger-Knuckle-Print Recognition via Encoding Local-Binary-Pattern”, Journal of Circuits, Systems and Computers, Vol. 22, No. 6, pp. 1-16, 2013.
- A Novel System based on Phase Congruency and Gabor - Filter Bank for Finger Knuckle Pattern Authentication
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Authors
Affiliations
1 Department of Computer Science, Mohamed El Bachir El Ibrahimi University, DZ
2 LMSE Laboratory, Mohamed El Bachir El Ibrahimi University, DZ
3 Department of Computer Science, Mohamed Boudiaf University, DZ
1 Department of Computer Science, Mohamed El Bachir El Ibrahimi University, DZ
2 LMSE Laboratory, Mohamed El Bachir El Ibrahimi University, DZ
3 Department of Computer Science, Mohamed Boudiaf University, DZ
Source
ICTACT Journal on Image and Video Processing, Vol 10, No 3 (2020), Pagination: 2125-2131Abstract
The authentication of individuals based on Finger Knuckle print (FKP) is a very interesting system in the biometric community. In this paper, we introduce a biometric authentication system based on the FKP trait which consists of four stages. The first one is the extraction of the Region of Interest (ROI). The Phase Congruency method with Gabor filters bank descriptors has been used in the feature extraction stage. Then to enhance the performance of the proposed scheme the Principle Component Analysis (PCA) + Linear Discriminant Analysis (LDA) method has been used in the dimensionality reduction stage. Finally, cosine Mahalanobis distance has been used in the matching stage. Experiments were conducted on the FKP PolyU Database which are publicly available. The reported results with comparison to previous methods prove the effectiveness of the proposed scheme, as well as the given system can achieve very high performance in both the identification and verification modes.Keywords
Finger Knuckle Print, Phase Congruency, Gabor Filters Bank, Score-Level-Fusion.References
- O.S. Adeoye, “A Survey of Emerging Biometric Technologies”, International Journal of Computer Applications, Vol. 9, No. 10, pp. 1-5, 2010.
- L. Zhang, L. Zhang, D. Zhang and H. Zhu, “Online Finger-Knuckle-Print Verification for Personal Authentication”, Pattern Recognition, Vol. 43, No. 7, pp. 2560-2571, 2010.
- S. Aoyama, K. Ito and T. Aoki, “A Finger-Knuckle-Print Recognition Algorithm using Phase-Based Local Block Matching”, Information Sciences, Vol. 268, No. 5, pp. 53-64, 2014.
- 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.
- Y. Zhai, H. Cao and L. Cao, “A Novel Finger-Knuckle-Print Recognition based on Batch-Normalized CNN”, Proceedings of Chinese Conference on Biometric Recognition, pp. 11-21, 2018
- G. Jaswal, R. Nath and A. Kaul, “FKP based Personal Authentication using SIFT Features Extracted from PIP Joint”, Proceedings of 3rd International Conference on Image Information Processing, pp. 214-219, 2015.
- A.B. Waghode and C.A. Manjare, “Biometric Authentication of Person using Finger Knuckle”, Proceedings of International Conference on Computing, Communication, Control and Automation, pp. 1-6, 2017.
- A. Nigam, K. Tiwari and P. Gupta, “Multiple Texture Information Fusion for Finger-Knuckle-Print Authentication System”, Neurocomputing, Vol. 188, pp. 190-205, 2016.
- W. Nunsong and K. Woraratpanya, “An Improved Finger-Knuckle-Print Recognition using Fractal Dimension based on Gabor Wavelet”, Proceedings of International Conference on International Joint Conference on Computer Science and Software Engineering, pp. 1-5, 2016.
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- A. Muthukumar and A. Kavipriya, “A Biometric System based on Gabor Feature Extraction with SVM Classifier for Finger-Knuckle-Print”, Pattern Recognition Letters, Vol. 125, pp. 150-156, 2019.
- 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. 03, pp. 1850-1857, 2018.
- 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.
- R. Chlaoua, A. Meraoumia, K.E. Aiadi and M. Korichi, “Deep Learning for Finger-Knuckle-Print Identification System based on PCANet and SVM Classifier”, Evolving Systems, Vol. 10, No. 2, pp. 261-272, 2019.
- D.R. Arun, C.C. Columbus and K. Meena, “Local Binary Patterns and Its Variants for Finger Knuckle Print Recognition in Multi-Resolution Domain”, Circuits and Systems, Vol. 7, No. 10, pp. 1-13, 2010.
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- A. Kong, “An Evaluation of Gabor Orientation as a Feature for Face Recognition”, Proceedings of 19th International Conference on Pattern Recognition, pp. 1-4, 2008.
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- P.N. Belhumeur, J.P. Hespanha and D.J. Kriegman, “Eigenfaces vs. Fisherfaces: Recognition using Class Specific Linear Projection”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 19, No. 7, pp. 711-720, 1997.
- B. Zeinali, A. Ayatollahi and M. Kakooei, “A Novel Method of Applying Directional Filter Bank (DFB) for Finger-Knuckle-Print (FKP) Recognition”, Proceedings of 22nd Iranian Conference on Electrical Engineering, pp. 500-504, 2014
- W. El Tarhouni, M.K. Shaikh, L. Boubchir and A. Bouridane, “Multi-Scale Shift Local Binary Pattern Based-Descriptor for Finger-Knuckle-Print Recognition”, Proceedings of 26th International Conference on Microelectronics, pp. 184-187, 2014.
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- 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.
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- 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.
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- 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.
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- 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
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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.
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- 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.
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- 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.
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- Ensemble of Preprocessing Techniques for 3D Palmprint Recognition with Collaborative Representation based Classification
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Authors
Affiliations
1 Computer Science Department, Mohamed El Bachir El Ibrahimi University of Bordj Bou Arreridj, DZ
2 Computer Science Department, Ferhat Abbas University, DZ
3 Department of Computer Science, University of Caen, FR
4 Ouargla University, DZ
1 Computer Science Department, Mohamed El Bachir El Ibrahimi University of Bordj Bou Arreridj, DZ
2 Computer Science Department, Ferhat Abbas University, DZ
3 Department of Computer Science, University of Caen, FR
4 Ouargla University, DZ
Source
ICTACT Journal on Image and Video Processing, Vol 11, No 1 (2020), Pagination: 2244-2250Abstract
3D Palmprint recognition has become a promising alternative tool for resolving problems compared to the robustness of 2D palmprint recognition. Regarding robustness, biometric systems that use 2D Palmprint suffer from being attacked by using a fake Palmprint identical. Given this, the current paper introduces a new 3D Palmprint recognition approach. Firstly, a set of preprocessing techniques has been applied on 3D depth image such as Tan and Triggs method which can effectively and efficiently eliminate the effect of the low-frequency component with keeping the local statistical properties of the processed image. Then, Gabor wavelets have been employed to extract features. After that, the extracted features have been used as an input in the collaborative representation based classification with regularized least squares (CRC_RLS) to classify the 3D Palmprint images. To evaluate its performance, the proposed algorithm has been applied on the PolyU 3D Palmprint database which contains 8.000 samples. The experimental results successfully and greatly improve the recognition results, especially when, we use Tan and Triggs method for preprocessing and Gabor for feature extraction with CRC_RLS for presentation and classification. We achieve a significant recognition rate of 100 % in lowest Runtime which reflects the robustness of the proposed recognition system.Keywords
Three-Dimensional Palmprint, Biometric, Gaussian Difference Filtering, Gradient Palms, Weberpalms, Gabor Features, Self-Quotient Image Algorithm.References
- S. Li and A. Jain, “Encyclopedia of Biometrics”, Springer, 2015.
- A. Attia and C. Mourad, “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.
- N.E. Chalabi, A. Attia and A. Bouziane, “Multimodal Finger Dorsal Knuckle Major and Minor Print Recognition system based on PCANET Deep Learning”, ICTACT Journal on Image and Video Processing, Vol. 10, No. 3, pp. 2153-2158, 2020.
- R. Hammouche, A. Attia and S. Akrouf, “A Novel System based on Phase Congruency and Gabor-Filter Bank for Finger Knuckle Pattern Authentication”, ICTACT Journal on Image and Video Processing, Vol. 10, no. 3, pp. 2125-2131, 2020.
- 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. 98, pp. 1-11, 2018.
- 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.
- 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.
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Authors
Affiliations
1 LMSE Laboratory, Mohamed El Bachir El Ibrahimi University of Bordj Bou Arreridj, DZ
2 Department of Network and Computer Security, State University of New York Polytechnic Institute, US
3 Department of Computer Science, Mohamed Boudiaf University, DZ
4 Department of Computer Science, Mohamed El Bachir El Ibrahimi University of Bordj Bou Arreridj, DZ
1 LMSE Laboratory, Mohamed El Bachir El Ibrahimi University of Bordj Bou Arreridj, DZ
2 Department of Network and Computer Security, State University of New York Polytechnic Institute, US
3 Department of Computer Science, Mohamed Boudiaf University, DZ
4 Department of Computer Science, Mohamed El Bachir El Ibrahimi University of Bordj Bou Arreridj, DZ