ICTACT Journal on Image and Video Processing

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Ensemble of Preprocessing Techniques for 3D Palmprint Recognition with Collaborative Representation based Classification


Affiliations
1 Computer Science Department, Mohamed El Bachir El Ibrahimi University of Bordj Bou Arreridj, Algeria
2 Computer Science Department, Ferhat Abbas University, Algeria
3 Department of Computer Science, University of Caen, France
4 Ouargla University, Algeria
     

   Subscribe/Renew Journal


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.
Subscription Login to verify subscription
User
Notifications
Font Size

  • 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.

  • 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.

  • J. Cui, “2D and 3D Palmprint Fusion and Recognition using PCA plus TPTSR Method”, Neural Computing and Applications, Vol. 24, No. 3-4. pp. 497-502, 2014.

  • A. Meraoumia, S. Chitroub and A. Bouridane, “2D and 3D Palmprint Information, PCA and HMM for an Improved Person Recognition Performance”, Integrated Computer Aided Engineering, Vol. 20, No. 3, pp. 303-319, 2013.

  • L. Zhang, Y. Shen, H. Li and J. Lu, “3D Palmprint Identification Using Block-Wise Features and Collaborative Representation”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 37, No. 8, pp. 1730-1736, 2104.

  • M. Chaa, N.E. Boukezzoula and A. Attia, “Score-Level Fusion of Two-Dimensional and Three-Dimensional Palmprint for Personal Recognition Systems”, Journal of Electronic Imaging, Vol. 26, No. 1, p. 13018-13024, 2017.

  • W. Li, D. Zhang, G. Lu and N. Luo, “A Novel 3-D Palmprint Acquisition System”, IEEE Transactions on Systems, Man, and Cybernetics Part A: Systems and Humans, Vol. 42, No. 2, pp. 443-452, 2011.

  • J.C. Russ and R.P. Woods, “Book Review. The Image Processing Handbook”, Journal of Computer Assisted Tomography, Vol. 19, No. 6, pp. 979-981, 1995.

  • X. Tan and W. Triggs, “Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions”, IEEE Transactions on Image Processing, Vol. 19, No. 6, pp. 1635-1650, 2010.

  • T. Zhang, Y.Y. Tang, B. Fang, Z. Shang and X. Liu, “Face Recognition under Varying Illumination using Gradient Faces”, IEEE Transactions on Image Processing, Vol. 18, No. 11, pp. 2599-2606, 2009.

  • B. Wang, W. Li, W. Yang and Q. Liao, “Illumination Normalization based on Weber’s Law with Application to Face Recognition”, IEEE Signal Processing Letters, Vol. 18, No. 8, pp. 462-465, 2011.

  • H. Wang, S.Z. Li, Y. Wang and J. Zhang, “Self Quotient Image for Face Recognition”, Proceedings of International Conference on Image Processing, pp. 1397-1400, 2004.

  • L. Shen and L. Bai, “A Review on Gabor Wavelets for Face Recognition”, Pattern Analysis and Applications, Vol. 9, No. 2-3, pp. 273-292, 2006.

  • L. Zhang, M. Yang and X. Feng, “Sparse Representation or Collaborative Representation: Which Helps Face Recognition?”, Proceedings of International Conference on Computer Vision, pp. 471-478, 2011.

  • N. Dalal and B. Triggs, “Histograms of Oriented Gradients for Human Detection”, Proceedings of International Conference on Computer Vision and Pattern Recognition, pp. 886-893, 2005.

  • D. Zhang, W.K. Kong, J. You and M. Wong, “Online Palmprint Identification”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 25, No. 9, pp. 1041-1050, 2003.


Abstract Views: 219

PDF Views: 0




  • Ensemble of Preprocessing Techniques for 3D Palmprint Recognition with Collaborative Representation based Classification

Abstract Views: 219  |  PDF Views: 0

Authors

Abdelouahab Attia
Computer Science Department, Mohamed El Bachir El Ibrahimi University of Bordj Bou Arreridj, Algeria
Abdelouahab Moussaoui
Computer Science Department, Ferhat Abbas University, Algeria
Youssef Chahir
Department of Computer Science, University of Caen, France
Mourad Chaa
Ouargla University, Algeria

Abstract


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