In this project you will explore the use of Principle Component Analysis (PCA) and Probabilistic PCA (PPCA). PPCA is closely-related to factor analysis, which is described in chapter 14 of your text. Our application is face recognition, following on the work of Moghadden and Pentland. A minimum version of this project would involve reading chapter 14 and conducting a face recognition experiment...

Human face-to-face communication plays an important role in human communication and interaction. In recent years, several different approaches have been proposed for developing methods of automatic facial expression analysis. In this paper we have proposed a novel facial expression recognition system which chooses the optimized features using particle swarm optimization (PSO) from the features ...

This paper explores the application of principal component analysis (PCA) to the monitoring of within-lot and between-lot plasma variations that occur in a plasma etching chamber used in semiconductor manufacturing, as observed through Optical Emission Spectroscopy (OES) analysis of the chamber exhaust. Using PCA, patterns that are difficult to identify in the 2048-dimension OES data are conden...

Visible/Near-infrared reflectance spectroscopy (Vis/NIRS) was applied to variety discrimination of juicy peach. A total of 75 samples were investigated for Vis/NIRS using a field spectroradiometer. Chemometrics was used to build the relationship between the absorbance spectra and varieties. Principle component analysis (PCA) was executed to reduce numerous wavebands into 8 principle components ...

Facial Expression Recognition is one of the active research area in the field of Human Machine Interaction (HMI) because of its several applications such as human emotion analysis, stress level and lie detection. In this paper, an algorithm for facial expression recognition has been proposed which integrate the Local Binary Patterns (LBP), Gabor filter and Principal Component Analysis (PCA). Th...

Principle Component Analysis (PCA) is an important and well-known technique of face recognition, where eigenvectors are used. In this paper, we propose a face recognition technique, which combines Eigenvectors with Singular Value Decomposition (SVD) techniques to reduce size of the Eigen-matrix. The detailed theoretical derivation and analysis are presented and a simulation results on Olivetti ...

This study presents an analysis on Visual Evoked Potentials (VEPs) recorded mainly from the occipital area of the brain. Accumulation of segmented windows (time locked averaging), Coiflet wavelet decomposition with dyadic filter bank and Principle Component Analysis (PCA) of three stages were utilized in order to decompose the recorded VEPs signal, to improve the Signal to Noise Ratio (SNR) and...

identification of homogenous watershed sub basins allows generalization of environmental study results. for this purpose, first available data for 27 selected watersheds in north alborz regarding 21 variables including physiographic and climatic characteristics was gathered. the most important factors impacting upon soil erosion and sediment yield were equivalent rectangular length, mean annual...

We propose an approach for performing adaptive principal component extraction. By this approach, the Least Mean Squared Error Reconstruction (LMSER) Principle is implemented in a successive way such that the reconstruction error is fedback as inputs for training the network's weights. Simulations results have shown that this type of LMSER implementation can perform Robust Principal Component An...