Feature fusion using Discriminant Correlation Analysis (DCA)

Feature fusion using Discriminant Correlation Analysis (DCA)

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
[e-mail]
PLEASE CITE THE ABOVE PAPER IF YOU USE THIS CODE.