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J. Wang, Y. Chen and M. Adjouadi, “A Comparative Study of Multilinear Principal Component Analysis for Face Recognition,” 37th IEEE Applied Image Pattern Recognition Workshop, 2008, pp. 1-6. doi:10.1109/AIPR.2008.4906476

has been cited by the following article:

• TITLE: A Genetic Programming-PCA Hybrid Face Recognition Algorithm

  AUTHORS: Behzad Bozorgtabar, Gholam Ali Rezai Rad

  KEYWORDS: Face Recognition, Principal Component Analysis, Genetic Programming, Leveraging Algorithm

  JOURNAL NAME: Journal of Signal and Information Processing, Vol.2 No.3, August 31, 2011

  ABSTRACT: Increasing demand for a fast and reliable face recognition technology has obliged researchers to try and examine different pattern recognition schemes. But until now, Genetic Programming (GP), acclaimed pattern recognition, data mining and relation discovery methodology, has been neglected in face recognition literature. This paper tries to apply GP to face recognition. First Principal Component Analysis (PCA) is used to extract features, and then GP is used to classify image groups. To further improve the results, a leveraging method is also utilized. It is shown that although GP might not be efficient in its isolated form, a leveraged GP can offer results comparable to other Face recognition solutions.