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

By Mohammad Haghighat, Mohamed Abdel-Mottaleb, Wadee Alhalabi Alhalabi. IEEE Transactions on Information Forensics and Security, 2016

343 citations recorded by the source corpus.

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 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 the 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 various biometric databases and using different feature extraction techniques, show the effectiveness of our proposed method, which outperforms other state-of-the-art approaches.

Subfields: Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/3b7a6291712f9796a3add49a3f65f0acaa3e1335

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In Computer Vision and Pattern Recognition

1. Weihua Li
2. Hongxun Yao
3. Yanfeng Gu
4. Zhuyun Chen
5. Huan Liu
6. Souleyman Chaib
7. Gong Cheng
8. Junwei Han

Citations in the corpus

• Multisensor Feature Fusion for Bearing Fault Diagnosis Using Sparse Autoencoder and Deep Belief Network (2017, background): “Therefore, it is believed to be more effective than the other two [11].”
• Deep Feature Fusion for VHR Remote Sensing Scene Classification (2017, background): “The between-class scatter matrix is defined in [42] as follows:”