Feature fusion using Canonical Correlation Analysis (CCA)

Version 1.0.1 (3.02 KB) by Mohammad Haghighat

Feature level fusion using Canonical Correlation Analysis (CCA)

https://github.com/mhaghighat/ccaFuse

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Updated 31 Jan 2020

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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.
CCAFUSE applies feature level fusion using a method based on Canonical Correlation Analysis (CCA). It gets the train and test data matrices from two modalities X and Y, and consolidates them into a single feature set Z.

Details can be found in:

M. Haghighat, M. Abdel-Mottaleb, W. Alhalabi, "Fully Automatic Face Normalization and Single Sample Face Recognition in Unconstrained Environments," Expert Systems With Applications, vol. 47, pp. 23-34, April 2016. http://dx.doi.org/10.1016/j.eswa.2015.10.047

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

Cite As

Haghighat, Mohammad, et al. “Fully Automatic Face Normalization and Single Sample Face Recognition in Unconstrained Environments.” Expert Systems with Applications, vol. 47, Elsevier BV, Apr. 2016, pp. 23–34, doi:10.1016/j.eswa.2015.10.047.

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MATLAB Release Compatibility

Created with R2015b

Compatible with any release

Platform Compatibility

Windows macOS Linux

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Acknowledgements

Inspired by: Dimensionality Reduction using Generalized Discriminant Analysis (GDA)

Inspired: Feature fusion using Discriminant Correlation Analysis (DCA)

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ccaFuse

Versions that use the GitHub default branch cannot be downloaded

Version	Published	Release Notes
1.0.1	31 Jan 2020	Updated the references
1.0.0.0	12 May 2016

To view or report issues in this GitHub add-on, visit the GitHub Repository.

MLA	Haghighat, Mohammad, et al. “Fully Automatic Face Normalization and Single Sample Face Recognition in Unconstrained Environments.” Expert Systems with Applications, vol. 47, Elsevier BV, Apr. 2016, pp. 23–34, doi:10.1016/j.eswa.2015.10.047.
APA	Haghighat, M., Abdel-Mottaleb, M., & Alhalabi, W. (2016). Fully automatic face normalization and single sample face recognition in unconstrained environments. Expert Systems with Applications, 47, 23–34. Elsevier BV. Retrieved from https://doi.org/10.1016%2Fj.eswa.2015.10.047
BibTeX	@article{Haghighat_2016, doi = {10.1016/j.eswa.2015.10.047}, url = {https://doi.org/10.1016%2Fj.eswa.2015.10.047}, year = 2016, month = {apr}, publisher = {Elsevier {BV}}, volume = {47}, pages = {23--34}, author = {Mohammad Haghighat and Mohamed Abdel-Mottaleb and Wadee Alhalabi}, title = {Fully automatic face normalization and single sample face recognition in unconstrained environments}, journal = {Expert Systems with Applications} }