research-article

Discriminant correlation analysis for feature level fusion with application to multimodal biometrics

Authors:

M. Abdel-Mottaleb,

W. AlhalabiAuthors Info & Claims

2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Pages 1866 - 1870

https://doi.org/10.1109/ICASSP.2016.7472000

Published: 01 March 2016 Publication History

Abstract

In this paper, we present Discriminant Correlation Analysis (DCA), a feature level fusion technique that incorporates the class associations in correlation analysis of the feature sets. DCA performs an effective feature fusion by maximizing the pair-wise correlations across the two feature sets, and at the same time, eliminating the between-class correlations and restricting the correlations to be within 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 realtime applications. Multiple sets of experiments performed on various biometric databases show the effectiveness of our proposed method, which outperforms other state-of-the-art approaches.

6. References

[1]

A. Ross and A.K. Jain, “Multimodal biometrics: An overview”, in 12th European Signal Processing Conference (EUSIPCO), 2004, pp. 1221–1224.

[2]

C. Liu and H. Wechsler, “A shape-and texture-based enhanced fisher classifier for face recognition”, IEEE Transactions on Image Processing, vol. 10, no. 4, pp. 598–608, 2001.

Digital Library

[3]

J. Yang, J. Yang, D. Zhang, and J. Lu, “Feature fusion: parallel strategy vs. serial strategy”, Pattern Recognition, vol. 36, no. 6, pp. 1369–1381, 2003.

[4]

Q.S. Sun, S.G. Zeng, Y. Liu, P.A. Heng, and D.S. Xia, “A new method of feature fusion and its application in image recognition”, Pattern Recognition, vol. 38, no. 12, pp. 2437–2448, 2005.

Digital Library

[5]

Y.O. Li, T. Adali, W. Wang, and V.D. Calhoun, “Joint blind source separation by multiset canonical correlation analysis”, IEEE Transactions on Signal Processing, vol. 57, no. 10, pp. 3918–3929, 2009.

Digital Library

[6]

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.

[7]

K.H. Pong and K.M. Lam, “Multi-resolution feature fusion for face recognition”, Pattern Recognition, vol. 47, no. 2, pp. 556–567, 2014.

Digital Library

[8]

M. Haghighat, M. Abdel-Mottaleb, and W. Alhalabi, “Fully automatic face normalization and single sample face recognition in unconstrained environments”, Expert Systems with Applications, vol. 47, pp. 23–34, 2016.

Digital Library

[9]

W.J. Krzanowski, Principles of multivariate analysis: a user's perspective, Oxford University Press, Inc., 1988.

Digital Library

[10]

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.

Digital Library

[11]

M. Turk and A. Pentland, “Eigenfaces for recognition”, Journal of Cognitive Neuroscience, vol. 3, no. 1, pp. 71–86, 1991.

Digital Library

[12]

R.O. Duda and P.E. Hart, Pattern classification and scene analysis, Wiley New York, 1973.

Digital Library

[13]

S. Crihalmeanu, A. Ross, S. Schuckers, and L. Hornak, “A protocol for multibiometric data acquisition, storage and dissemination”, Technical Report, WVU, Lane Department of Computer Science and Electrical Engineering, 2007.

[14]

A. Martinez and R. Benavente, “The AR face database”, CVC Technical Report, vol. 24, 1998.

[15]

S. Shekhar, V.M. Patel, N.M. Nasrabadi, and R. Chellappa, “Joint sparse representation for robust multimodal biometrics recognition”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 36, no. 1, pp. 113–126, 2014.

Digital Library

[16]

S.J. Pundlik, D.L. Woodard, and S.T. Birchfield, “Non-ideal iris segmentation using graph cuts”, in IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2008, pp. 1–6.

[17]

L. Masek and P. Kovesi, “Matlab source code for a biometric identification system based on iris patterns”, The School of Computer Science and Software Engineering, The University of Western Australia, vol. 26, 2003.

[18]

S. Chikkerur, C. Wu, and V. Govindaraju, “A systematic approach for feature extraction in fingerprint images”, in Biometric Authentication, pp. 344–350. Springer, 2004.

[19]

A.K. Jain, S. Prabhakar, L. Hong, and S. Pankanti, “Filterbank-based fingerprint matching”, IEEE Transactions on Image Processing, vol. 9, no. 5, pp. 846–859, 2000.

Digital Library

[20]

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.

Digital Library

[21]

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.

[22]

M. Haghighat, S. Zonouz, and M. Abdel-Mottaleb, “CloudID: Trustworthy cloud-based and cross-enterprise biometric identification”, Expert Systems with Applications, vol. 42, no. 21, pp. 7905–7916, 2015.

Digital Library

[23]

U. Park, R. Jillela, A. Ross, and A.K. Jain, “Periocular biometrics in the visible spectrum”, IEEE Transactions on Information Forensics and Security, vol. 6, no. 1, pp. 96–106, 2011.

Digital Library

[24]

M. Balasubramanian, S. Palanivel, and V. Ramalingam, “Real time face and mouth recognition using radial basis function neural networks”, Expert Systems with Applications, vol. 36, no. 3, pp. 6879–6888, 2009.

Digital Library

[25]

A.M. Martinez and A.C. Kak, “PCA versus LDA”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 2, pp. 228–233, 2001.

Digital Library

[26]

H. Li, K.A. Toh, and L. Li, Advanced topics in biometrics, World Scientific, 2012.

Cited By

Zhang HShi HHou JXiong QJi D(2022)Image Sentiment Analysis via Active Sample Refinement and Cluster Correlation MiningComputational Intelligence and Neuroscience10.1155/2022/24776052022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/2477605
Zhang HXiong QShi HLiang WZhong XZhu ZLi XJi D(2021)Mining Sufficient Knowledge via Progressive Feature Fusion for Efficient Material RecognitionScientific Programming10.1155/2021/89713492021Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1155/2021/8971349
Yu CWang HLiu XTao L(2018)Enhanced Linear Discriminant Canonical Correlation Analysis for Cross-modal Fusion RecognitionAdvances in Multimedia Information Processing – PCM 201810.1007/978-3-030-00776-8_77(841-853)Online publication date: 21-Sep-2018
https://dl.acm.org/doi/10.1007/978-3-030-00776-8_77

Index Terms

Discriminant correlation analysis for feature level fusion with application to multimodal biometrics
1. Applied computing

Index terms have been assigned to the content through auto-classification.

Recommendations

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Guide Proceedings

2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

6592 pages

Copyright © 2016.

Publisher

IEEE Press

Publication History

Published: 01 March 2016

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 14 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Zhang HShi HHou JXiong QJi D(2022)Image Sentiment Analysis via Active Sample Refinement and Cluster Correlation MiningComputational Intelligence and Neuroscience10.1155/2022/24776052022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/2477605
Zhang HXiong QShi HLiang WZhong XZhu ZLi XJi D(2021)Mining Sufficient Knowledge via Progressive Feature Fusion for Efficient Material RecognitionScientific Programming10.1155/2021/89713492021Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1155/2021/8971349
Yu CWang HLiu XTao L(2018)Enhanced Linear Discriminant Canonical Correlation Analysis for Cross-modal Fusion RecognitionAdvances in Multimedia Information Processing – PCM 201810.1007/978-3-030-00776-8_77(841-853)Online publication date: 21-Sep-2018
https://dl.acm.org/doi/10.1007/978-3-030-00776-8_77

View Options

View options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Table of Contents