Feature fusion using Discriminant Correlation Analysis (DCA)

版本 1.0.1 (3.8 KB) 作者: Mohammad Haghighat
Feature fusion using Discriminant Correlation Analysis (DCA)
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更新时间 2020/1/31

Feature fusion is the process of combining two feature vectors to obtain a single feature vector, which is more discriminative than any of the input feature vectors.
DCAFUSE applies feature level fusion using a method based on Discriminant Correlation Analysis (DCA). It gets the train and test data matrices from two modalities X and Y, along with their corresponding class labels and consolidates them into a single feature set Z.

Details can be found in:

M. Haghighat, M. Abdel-Mottaleb, W. Alhalabi, "Discriminant Correlation Analysis: Real-Time Feature Level Fusion for Multimodal Biometric Recognition," IEEE Transactions on Information Forensics and Security, vol. 11, no. 9, pp. 1984-1996, Sept. 2016.
http://dx.doi.org/10.1109/TIFS.2016.2569061

and

M. Haghighat, M. Abdel-Mottaleb W. Alhalabi, "Discriminant Correlation Analysis for Feature Level Fusion with application to multimodal biometrics," IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2016, pp. 1866-1870.
http://dx.doi.org/10.1109/ICASSP.2016.7472000


(C) Mohammad Haghighat, University of Miami
haghighat@ieee.org
PLEASE CITE THE ABOVE PAPER IF YOU USE THIS CODE.

引用格式

Haghighat, Mohammad, et al. “Discriminant Correlation Analysis: Real-Time Feature Level Fusion for Multimodal Biometric Recognition.” IEEE Transactions on Information Forensics and Security, vol. 11, no. 9, Institute of Electrical and Electronics Engineers (IEEE), Sept. 2016, pp. 1984–96, doi:10.1109/tifs.2016.2569061.

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