A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf
.
Face Recognition From Single Sample Per Person by Learning of Generic Discriminant Vectors
2012
Procedia Engineering
The conventional ways of recognizing faces always assume the possession and heavily relies on extensive and representative datasets, but that is not the case in most real-world situations where more often than not, a very limited or even only single sample per person (SSPP) is available which ultimately rendering most face recognition systems to fail severely. This paper proposes a development of face recognition based on a combination of traditional eigenface with artificial neural network
doi:10.1016/j.proeng.2012.07.199
fatcat:ys6wjisrqvbm3mnldxvazl32va