Feature fusion using Canonical Correlation Analysis (CCA)

Versión 1.0.1 (3,02 KB) por Mohammad Haghighat

Feature level fusion using Canonical Correlation Analysis (CCA)

https://github.com/mhaghighat/ccaFuse

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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.

Citar como

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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Se creó con R2015b

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Inspirado por: Dimensionality Reduction using Generalized Discriminant Analysis (GDA)

Inspiración para: Feature fusion using Discriminant Correlation Analysis (DCA)

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ccaFuse

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Versión	Publicado	Notas de la versión
1.0.1	31 ene 2020	Updated the references
1.0.0.0	12 may 2016

Para consultar o notificar algún problema sobre este complemento de GitHub, visite el repositorio de 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} }