Discriminant Correlation Analysis: Real-Time Feature Level Fusion for Multimodal Biometric Recognition

@article{Haghighat2016DiscriminantCA,
  title={Discriminant Correlation Analysis: Real-Time Feature Level Fusion for Multimodal Biometric Recognition},
  author={Mohammad Haghighat and Mohamed Abdel-Mottaleb and Wadee S. Alhalabi},
  journal={IEEE Transactions on Information Forensics and Security},
  year={2016},
  volume={11},
  pages={1984-1996}
}
Information fusion is a key step in multimodal biometric systems. The 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… 
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References

SHOWING 1-10 OF 68 REFERENCES
Discriminant correlation analysis for feature level fusion with application to multimodal biometrics
TLDR
Discriminant Correlation Analysis (DCA), a feature level fusion technique that incorporates the class associations in correlation analysis of the feature sets, is presented, which outperforms other state-of-the-art approaches.
Score normalization in multimodal biometric systems
TLDR
Study of the performance of different normalization techniques and fusion rules in the context of a multimodal biometric system based on the face, fingerprint and hand-geometry traits of a user found that the application of min-max, z-score, and tanh normalization schemes followed by a simple sum of scores fusion method results in better recognition performance compared to other methods.
Multimodal Biometric System Using Rank-Level Fusion Approach
  • M. Monwar, M. Gavrilova
  • Computer Science, Medicine
    IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)
  • 2009
TLDR
An effective fusion scheme that combines information presented by multiple domain experts based on the rank-level fusion integration method is presented and results indicate that fusion of individual modalities can improve the overall performance of the biometric system, even in the presence of low quality data.
Multi-resolution feature fusion for face recognition
TLDR
A novel face recognition approach using information about face images at higher and lower resolutions so as to enhance the information content of the features that are extracted and combined at different resolutions and employs the cascaded generalized canonical correlation analysis (GCCA).
A new method of feature fusion and its application in image recognition
TLDR
Experimental results on Concordia University CENPARMI database of handwritten Arabic numerals and Yale face database show that recognition rate is far higher than that of the algorithm adopting single feature or the existing fusion algorithm.
Feature Fusion Method Based on KCCA for Ear and Profile Face Based Multimodal Recognition
  • Xiaona Xu, Zhichun Mu
  • Computer Science
    2007 IEEE International Conference on Automation and Logistics
  • 2007
TLDR
A novel feature fusion method based on kernel canonical correlation analysis (KCCA) is presented and applied to ear and profile face based multimodal biometrics for personal recognition, which provides a new effective approach of non- intrusive biometric recognition.
Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition
TLDR
A novel Gabor-Fisher (1936) classifier (GFC) for face recognition is introduced, which applies the enhanced Fisher linear discriminant model (EFM) to an augmented Gabor feature vector derived from the Gabor wavelet representation of face images.
Joint Sparse Representation for Robust Multimodal Biometrics Recognition
TLDR
A multimodal sparse representation method, which represents the test data by a sparse linear combination of training data, while constraining the observations from different modalities of the test subject to share their sparse representations, which simultaneously takes into account correlations as well as coupling information among biometric modalities.
Feature-level fusion of fingerprint and finger-vein for personal identification
TLDR
Experimental results show that the proposed approach has a high capability in fingerprint-vein based personal recognition as well as multimodal feature-level fusion.
Discriminative Learning and Recognition of Image Set Classes Using Canonical Correlations
TLDR
A novel discriminative learning method over sets is proposed for set classification that maximizes the canonical correlations of within-class sets and minimizes thecanon correlations of between- class sets.
...
1
2
3
4
5
...