Feature fusion using Canonical Correlation Analysis (CCA)

Feature level fusion using Canonical Correlation Analysis (CCA)

https://github.com/mhaghighat/ccaFuse

Mohammad Haghighat

Version 1.0.1 (3,02 ko)

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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.

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Citation pour cette source

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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Inspiré par : Dimensionality Reduction using Generalized Discriminant Analysis (GDA)

A inspiré : Feature fusion using Discriminant Correlation Analysis (DCA)

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Version 1.0.1 (3,02 ko)
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Version	Publié le	Notes de version	Action
1.0.1	31 jan. 2020	Updated the references
1.0.0.0	12 mai 2016

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

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} }