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Reseach Article

Feature Level fusion based on Conical Correlation Analysis and Discriminant Correlation Analysis for Palm Print and Hand Vein

by Shreyas Rangappa, Naveena C., H. K. Chethan, G. Hemantha Kumar
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 181 - Number 9
Year of Publication: 2018
Authors: Shreyas Rangappa, Naveena C., H. K. Chethan, G. Hemantha Kumar
10.5120/ijca2018917615

Shreyas Rangappa, Naveena C., H. K. Chethan, G. Hemantha Kumar . Feature Level fusion based on Conical Correlation Analysis and Discriminant Correlation Analysis for Palm Print and Hand Vein. International Journal of Computer Applications. 181, 9 ( Aug 2018), 47-51. DOI=10.5120/ijca2018917615

@article{ 10.5120/ijca2018917615,
author = { Shreyas Rangappa, Naveena C., H. K. Chethan, G. Hemantha Kumar },
title = { Feature Level fusion based on Conical Correlation Analysis and Discriminant Correlation Analysis for Palm Print and Hand Vein },
journal = { International Journal of Computer Applications },
issue_date = { Aug 2018 },
volume = { 181 },
number = { 9 },
month = { Aug },
year = { 2018 },
issn = { 0975-8887 },
pages = { 47-51 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume181/number9/29804-2018917615/ },
doi = { 10.5120/ijca2018917615 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:05:32.578385+05:30
%A Shreyas Rangappa
%A Naveena C.
%A H. K. Chethan
%A G. Hemantha Kumar
%T Feature Level fusion based on Conical Correlation Analysis and Discriminant Correlation Analysis for Palm Print and Hand Vein
%J International Journal of Computer Applications
%@ 0975-8887
%V 181
%N 9
%P 47-51
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Information fusion is a key step in multimodal biometric systems. Fusion of information can occur at different levels of a recognition system, i.e., at the feature level, matching-score level, or decision level. However, feature level fusion is believed to be more effective owing to the fact that a feature set contains richer information about the input biometric data than the matching score or the output decision of a classifier. The goal of feature fusion for recognition is to combine relevant information from two or more feature vectors into a single one with more discriminative power than any of the input feature vectors. In pattern recognition problems, we are also interested in separating the classes. In this paper, we present Discriminant Correlation Analysis (DCA), a feature level fusion technique that incorporates the class associations into the correlation analysis of the feature sets. DCA performs an effective feature fusion by maximizing the pairwise correlations across the two feature sets, and at the same time, eliminating the between-class correlations and restricting the correlations to be within the classes. Our proposed method can be used in pattern recognition applications for fusing features extracted from multiple modalities or combining different feature vectors extracted from a single modality. It is noteworthy that DCA is the first technique that considers class structure in feature fusion. Moreover, it has a very low computational complexity and it can be employed in real-time applications. Multiple sets of experiments performed on Palm print and Hand vein datasets, and using different feature extraction techniques.

References
  1. S Veluchamy, L R Karlmarx,: System for Multimodal Biometric recognition based on Finger Knuckle and Finger Vein using Feature Level Fusion and K-support vector Machine Classifier, IEEE Biometrics, Vol 6 (2017).
  2. Gopal, Smriti Srivastava, Saurabh Bhardwaj, Sandeep Bhargava,: Fusion of palm-phalanges print with palmprint and dorsal hand vein, Elsevier, Applied Soft Computing 47, 2016, 12-20.
  3. M. Haghighat, M. Abdel-Mottaleb, W. Alhalabi,: Discriminant Correlation Analysis for Feature Level Fusion with Application to Multimodal Biometrics, IEEE,Speech and Signal Processing (ICASSP), 2016.
  4. Xiaofeng, Yang, Dongmei,: Feature -Level Fusion of palmprint and palm vein Base on Canonical Correlation Analysis, IEEE, Signal Processing (ICSP), 2016.
  5. Bharathi, S, R Sudhakar Valentina E Balas,: Hand Vein-based Multimodal Biometric Recognition, ActaPolytechnicaHungarica, (2015) 213-229.
  6. D Vaidya, S Pawar, Madhuri A Joshi, A M Sapkal, S kar,: Feature level Fusion of Palm Print and Palm Vein for person Authentication Based on Entropy Technique, International Journal of electronics and Communication Technology,(2014) Vol.5.
  7. Li Peidan, Miao Zhenjiang, Wang Zhiqun,: Fusion of Palm Print and Palm Vein Images for Person Recognition, IEEE, Signal Processing(ICSP), 2014.
  8. D P Gaikwad, S P Narote,: Multi-Modal Biometric System using Palm Print and Palm Vein Features, Annual IEEE Indian Conference(INDICON), (2013).
  9. M Heenaye, M Khan,: A Multimodal HandVein Biometric based on Score Level Fusion, Internaional Symposium on Robotics and Intelligent Sensors(IRIS), Elsevier, (2012).
  10. Ashok Rao, M Imran, R Ragavendra, Hemantha Kumar G,: Multibiometrics: Analysis and Robustness of Hand Vein and Palm Print Combination Used for Person Verification, International Journal for Emerging Trends in Engineering and Technology (IJETET), (2011).
  11. N.M. Correa, T. Adali, Y. Li, and V.D. Calhoun, “Canonical correlation analysis for data fusion and group inferences,” IEEE Signal Processing Magazine, vol. 27, no. 4, pp. 39–50, 2010.
  12. P.N. Belhumeur, J.P. Hespanha, and D. 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.
  13. H. Li, K.A. Toh, and L. Li, Advanced topics in biometrics, World Scientific, 2012.
  14. M. Turk and A. Pentland, “Eigenfaces for recognition,” Journal of Cognitive Neuroscience, vol. 3, no. 1, pp. 71– 86, 1991.
  15. R.O. Duda and P.E. Hart, Pattern classification and scene analysis, Wiley New York, 1973.
  16. C. Liu and H. Wechsler, “Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition,” IEEE Transactions on Image processing, vol. 11, no. 4, pp. 467–476, 2002.
  17. N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 1, 2005, pp. 886–893.
  18. H. Bay, A. Ess, T. Tuytelaars, and L. Van Gool, “Speeded-Up Robust Features (SURF),” Computer Vision and Image Understanding, vol. 110, no. 3, pp. 346–359, 2008.
  19. M. Haghighat, S. Zonouz, and M. Abdel-Mottaleb, “Identification using encrypted biometrics,” in Computer Analysis of Images and Patterns (CAIP). Springer, 2013, pp. 440–448.
  20. P. F. Felzenszwalb, R. B. Girshick, D. McAllester, and D. Ramanan, “Object detection with discriminatively trained part-based models,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 9, pp. 1627–1645, 2010.
Index Terms

Computer Science
Information Sciences

Keywords

multimodal biometrics feature level fusion class structure discriminant correlation analysis.