Feature fusion using Discriminant Correlation Analysis (DCA)

Version 1.0.1 (3,82 ko) par Mohammad Haghighat

https://github.com/mhaghighat/dcaFuse

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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.

Citation pour cette source

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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Inspiré par : Dimensionality Reduction using Generalized Discriminant Analysis (GDA), Feature fusion using Canonical Correlation Analysis (CCA)

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dcaFuse

Les versions qui utilisent la branche GitHub par défaut ne peuvent pas être téléchargées

Version	Publié le	Notes de version
1.0.1	31 jan. 2020	Updated the README
1.0.0.0	16 fév. 2017	Added the references Added the references

Version

Publié le

Notes de version

1.0.1

31 jan. 2020

Updated the README

1.0.0.0

16 fév. 2017

Added the references

Pour consulter ou signaler des problèmes liés à ce module complémentaire GitHub, accédez au dépôt GitHub.

MLA	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.
APA	Haghighat, M., Abdel-Mottaleb, M., & Alhalabi, W. (2016). Discriminant Correlation Analysis: Real-Time Feature Level Fusion for Multimodal Biometric Recognition. IEEE Transactions on Information Forensics and Security, 11(9), 1984–1996. Institute of Electrical and Electronics Engineers (IEEE). Retrieved from https://doi.org/10.1109%2Ftifs.2016.2569061
BibTeX	@article{Haghighat_2016, doi = {10.1109/tifs.2016.2569061}, url = {https://doi.org/10.1109%2Ftifs.2016.2569061}, year = 2016, month = {sep}, publisher = {Institute of Electrical and Electronics Engineers ({IEEE})}, volume = {11}, number = {9}, pages = {1984--1996}, author = {Mohammad Haghighat and Mohamed Abdel-Mottaleb and Wadee Alhalabi}, title = {Discriminant Correlation Analysis: Real-Time Feature Level Fusion for Multimodal Biometric Recognition}, journal = {{IEEE} Transactions on Information Forensics and Security} }