Protonation-dependent heterogeneity in fluorescent binding sites in sub-fractions of fulvic acid using principle component analysis and two-dimensional correlation spectroscopy

The task of face recognition has been actively researched in recent years because of its many applications in various domains. This paper presents a robust face recognition system using curvelet-based two-dimensional principle component analysis (2D PCA) to address the problem of human face recognition from still images. 2D PCA has advantages over PCA in evaluating the covariance matrix accurately and time complexity. Inspired by the attractive attributes of curvelets in catching the edge singularities with very few coefficients in a non-adaptive manner, we introduce the scheme of decomposing images into curvelet subbands and applying 2D PCA to create a representative feature set. Experiments were designed with different implementations of each module using standard testing database. We experimented with changing the illumination normalization procedure; comparing the baseline PCA-based method with the proposed scheme; studying effects on algorithm performance of k-nearest neighbor (kNN) classifier and Support Vector Machine (SVM) classifier in the classification process; also we experimented with different databases such as FERET, etc. High accuracy rate were achieved by the proposed scheme through a comparative study.

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Two Dimensional Principle Component Analysis of Gabor features for face representation and recognition

2008 6th International Symposium on Communication Systems, Networks and Digital Signal Processing ◽

10.1109/csndsp.2008.4610739 ◽

2008 ◽

Cited By ~ 2

Author(s):

R.M. Mutelo ◽

W.L. Woo ◽

S.S. Dlay

Keyword(s):

Principle Component Analysis ◽

Component Analysis ◽

Two Dimensional ◽

Face Representation ◽

Principle Component ◽

Gabor Features

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ANALYSIS OF VOICE CUES IN RECOGNITION OF SARCASM

Recent Patents on Computer Science ◽

10.2174/2213275912666190819113541 ◽

2019 ◽

Vol 12 ◽

Cited By ~ 1

Author(s):

Basavaraj N Hiremath ◽

Malini M Patilb

Keyword(s):

Feature Extraction ◽

Computer Program ◽

Principle Component Analysis ◽

Voice Recognition ◽

Component Analysis ◽

Recognition System ◽

Principle Component ◽

Whole Process ◽

The Voice

The voice recognition system is about cognizing the signals, by feature extraction and identification of related parameters. The whole process is referred to as voice analytics. The paper aims at analysing and synthesizing the phonetics of voice using a computer program called “PRAAT”. The work carried out in the paper also supports the analysis of voice segmentation labelling, analyse the unique features of voice cues, understanding physics of voice, further the process is carried out to recognize sarcasm. Different unique features identified in the work are, intensity, pitch, formants related to read, speak, interactive and declarative sentences by using principle component analysis.

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Four challenges for cognitive neuroscience and the cortico-hippocampal division of memory

Behavioral and Brain Sciences ◽

10.1017/s0140525x03370153 ◽

2003 ◽

Vol 26 (6) ◽

pp. 681-682

Author(s):

Harry Howard

Keyword(s):

Independent Component Analysis ◽

Recent Work ◽

Cognitive Neuroscience ◽

Principle Component Analysis ◽

Pattern Analysis ◽

Component Analysis ◽

Independent Component ◽

Principle Component ◽

Complementary Methods ◽

Current State

Jackendoff's criticisms of the current state of theorization in cognitive neuroscience are defused by recent work on the computational complementarity of the hippocampus and neocortex. Such considerations lead to a grounding of Jackendoff's processing model in the complementary methods of pattern analysis effected by independent component analysis (ICA) and principle component analysis (PCA).

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