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, 2016.

(C) Mohammad Haghighat, University of Miami

haghighat@ieee.org

PLEASE CITE THE ABOVE PAPER IF YOU USE THIS CODE.