[PDF][PDF] Sparse random projection and dimensionality reduction applied on face recognition
N Belghini, A Zarghili, J Kharroubi… - The Proceedings of …, 2011 - academia.edu
N Belghini, A Zarghili, J Kharroubi, A Majda
The Proceedings of International Conference on Intelligent Systems …, 2011•academia.eduIn the domain of face recognition, many methods are used to reduce the dimensionality of
the subspace in which faces are presented. Recently, Random Projection (RP) has emerged
as a powerful method for dimensionality reduction. It represents a computationally simple
and efficient method that preserves the structure of the data without introducing very
significant distortion. Our focus in this paper is to investigate the dimensionality reduction
offered by RP and perform an artificial intelligent system for face recognition using …
the subspace in which faces are presented. Recently, Random Projection (RP) has emerged
as a powerful method for dimensionality reduction. It represents a computationally simple
and efficient method that preserves the structure of the data without introducing very
significant distortion. Our focus in this paper is to investigate the dimensionality reduction
offered by RP and perform an artificial intelligent system for face recognition using …
Abstract
In the domain of face recognition, many methods are used to reduce the dimensionality of the subspace in which faces are presented. Recently, Random Projection (RP) has emerged as a powerful method for dimensionality reduction. It represents a computationally simple and efficient method that preserves the structure of the data without introducing very significant distortion.
Our focus in this paper is to investigate the dimensionality reduction offered by RP and perform an artificial intelligent system for face recognition using backpropagation neural network. Experiments show that projecting the data onto a random lower-dimensional subspace yields results and give an acceptable face recognition rate.
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