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… 

Palm Vein Recognition Based on Competitive Code, LBP and DCA Fusion Strategy

The DCA combines the two characteristics of the palm vein, which not only shortens the classification time in some degree, but also improves the recognition rate to 99.8% in the case of training samples of 9 and test samples of 3.

Kernel Discriminant Correlation Analysis: Feature Level Fusion for Nonlinear Biometric Recognition

Kernel-DCA is proposed, which generalizes DCA in order to handle nonlinear problems and utilizes a kernel method to map feature sets to a high-dimensional space in which features are linearly separable.

Iris-Fingerprint multimodal biometric system based on optimal feature level fusion model

It is demonstrated that an efficient multimodal recognition can be achieved with a significant reduction in feature dimensions with less computational complexity and recognition time less than one second by exploiting CCA based joint feature fusion and optimization.

A Multi-Biometric System Based on Feature and Score Level Fusions

It is shown that the multi-biometric system based on the proposed fusion scheme provides the best performance when it employs the new normalization technique and the confidence-based weighting (CBW) method.

Multimodal Biometrics Using Fingerprint, Palmprint, and Iris With a Combined Fusion Approach

The experiments performed on 100 different subjects from publicly available databases demonstrate that combining featurelevel with match score level and feature level with decision level fusion both outperforms fusion at only a feature level.

Multimodal System Framework for Feature Level Fusion based on CCA with SVM Classifier

It is demonstrated that an efficient multimodal classification can be achieved with a significant reduction in the number of feature dimensions by exploiting canonical correlation analysis on the extracted feature sets from iris and fingerprint modalities.

A Hybrid Approach to Multimodal Biometric Recognition Based on Feature-level Fusion of Face, Two Irises, and Both Thumbprints

The results show that the hybrid multimodal template, while being secure against spoof attacks and making the system robust, can use the dimensionality of only 15 features to increase the accuracy of a hybrid multimodeal biometric system to 100%, which shows a significant improvement compared with uni-biometric and other multimodAL systems.

Deep multimodal biometric recognition using contourlet derivative weighted rank fusion with human face, fingerprint and iris images

A deep multimodal biometric system for human recognition using three traits, face, fingerprint and iris, with the objective of reducing the feature vector dimension in the temporal domain and a deep learning framework is presented for improving the recognition rate.

A Multi-Biometric System Based on Multi-Level Hybrid Feature Fusion

Numerical results have proved that the proposed multimodal biometric recognition system based on a multi-level hybrid feature fusion mechanism to compact knowledge from multiple feature vectors outperformed other state of the art recent variants.

Multimodal Biometrics via Discriminant Correlation Analysis on Mobile Devices

An approach for combining features from different modalities, also known as feature-level fusion, for mobile devices in which the degradation of biometric image quality due to uncontrolled conditions and limited computational resources poses unique challenges is proposed.
...

References

SHOWING 1-10 OF 68 REFERENCES

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

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.

Multimodal Biometric System Using Rank-Level Fusion Approach

  • M. MonwarM. Gavrilova
  • Computer Science
    IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)
  • 2009
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.

Feature Fusion Method Based on KCCA for Ear and Profile Face Based Multimodal Recognition

  • Xiaona XuZhichun Mu
  • Computer Science
    2007 IEEE International Conference on Automation and Logistics
  • 2007
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

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

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.

Discriminative Learning and Recognition of Image Set Classes Using Canonical Correlations

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.
...