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 Independent Component Representations for Face Recognition
Keywords: Independent component analysis, ICA, principal component analysis, PCA, face recognition. 1. INTRODUCTION Several advances in face recognition such as "H~lons,~ " "Eigenfa~es,~ " and "Local Feature Analysis4" have employed forms of principal component analysis, which addresses only second-order moments of the input. Principal component...
Face recognition using Principal Component Analysis
Facial recognition can be done by various methods such as principal component analysis (PCA), linear discriminant analysis (LDA), independent component analysis (ICA), a local binary pattern ...
Independent component analysis for face recognition based ...
In this paper a new method has been proposed based on the combination of principal component analysis(PCA) and independent component analysis (ICA) for face recognition.
RFID and Face Recognition Based Security and Access ...
m) Jun-Ying Gan, Si-Bin He on Proceedings of the Eighth International Conference on Machine Learning and Cybernetics, proposed an improved 2DPCA algorithm for face recognition. on the basis of two-dimensional principal component analysis, an improved two-dimensional principal component analysis (I2DPCA) is presented for face recognition....
Face Recognition Using Incremental Principal Component ...
Keywords— IPCA-ICA, Principal component analysis (PCA), independent component analysis (ICA), principal non-Gaussian directions, image processing, blind source separation. I. INTRODUCTION A large number of face recognition techniques use face representations found by unsupervised statistical methods....
RESEARCH ARTICLE An Investigation of Face Recognition ...
This aims to investigate the face recognition characteristics using widely adopted statistical approaches (i.e., Principal Component Analysis (PCA) and Independent Component Analysis (ICA)). This paper focus on Eigen faces approach for implementing the face recognition and detection on the images to compare the performance of PCA and ICA....
Principal Manifolds and Probabilistic Subspaces for Visual ...
We investigate the use of linear and nonlinear principal manifolds for learning low-dimensional representations for visual recognition. Several leading techniques: Principal Component Analysis (PCA), Independent Component Analysis (ICA), and nonlinear Kernel PCA (KPCA) are examined and tested in a visual recognition experiment using 1,800+ facial images from the "FERET"database....
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