Upd: Updated the reference

README.md

@@ -5,9 +5,15 @@ DCAFUSE applies feature level fusion using a method based on Discriminant Correl
 
 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.
+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
 
-(C)	Mohammad Haghighat, University of Miami
-      	[email protected]
-      	PLEASE CITE THE ABOVE PAPER IF YOU USE THIS CODE.
+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
+        [email protected]
+        PLEASE CITE THE ABOVE PAPERS IF YOU USE THIS CODE.

dcaFuse.m

@@ -4,14 +4,14 @@
 % 
 % 
 % Inputs:
-%       X       :	pxn matrix containing the first set of training feature vectors
+%       X       :   pxn matrix containing the first set of training feature vectors
 %                   p:  dimensionality of the first feature set
 %                   n:  number of training samples
 % 
-%       Y       :	qxn matrix containing the second set of training feature vectors
+%       Y       :   qxn matrix containing the second set of training feature vectors
 %                   q:  dimensionality of the second feature set
 % 
-%       label   :	1xn row vector of length n containing the class labels
+%       label   :   1xn row vector of length n containing the class labels
 %               
 % Outputs:
 %       Ax  :   Transformation matrix for the first data set (rxp)
@@ -41,16 +41,28 @@
 %   >> testZ  = [testXcca + testYcca];
 % 
 % 
+% 
 %   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.
+%   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
+% (C)   Mohammad Haghighat, University of Miami
 %       [email protected]
-%       PLEASE CITE THE ABOVE PAPER IF YOU USE THIS CODE.
+%       PLEASE CITE THE ABOVE PAPERS IF YOU USE THIS CODE.
 
 
 [p,n] = size(X);