Comparison of PCA, LDA and Gabor Features for Face Recognition Using Fuzzy Neural Network

Pankaj, Dhanya S; Wilscy, M.

doi:10.1007/978-3-642-31552-7_43

Cited by 9 publications

(3 citation statements)

References 13 publications

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“…The purpose of this computation is to maximize the distance between two classes of data. Hence, x belongs to one class with the largest value of decision function as depicted in (8).…”

Section: Multi-class Support Vector Machinementioning

confidence: 99%

“…Two primary processes in face recognition areas are feature extraction and recognition or classification. In the past years, many researchers had used Principal Component Analysis (PCA) or known as Karhunen-Loeve method for face recognition purpose [6]- [8]. The main idea of this algorithm is representing the significant variations in facial images in a lower dimensionality size.…”

Section: Introductionmentioning

confidence: 99%

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Cognitive-Affective Emotion Classification: Comparing Features Extraction Algorithm Classified by Multi-class Support Vector Machine

Diana¹,

Sabiq²

2016

IJCCE

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Emotional quotient (EQ) is one of the main factors determining the outcome of a learning process. A cognitive-affective states that usually appear during a learning process are bored, confuse, and excited/enthusiastic. Emotion state can be detected by identifying human facial expressions. Here, Principal Component analysis (PCA) and Gabor features extract salient information from facial expression database. Each feature space obtained from these methods is then classified using multi-class Support Vector Machine (SVM) with two cross-validation methods, Holdout and 10-fold cross validation. Experiment results show that classification process using Gabor features and 10-fold cross validation of multi-class SVM give the best accuracy rate.

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“…The purpose of this computation is to maximize the distance between two classes of data. Hence, x belongs to one class with the largest value of decision function as depicted in (8).…”

Section: Multi-class Support Vector Machinementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Cognitive-Affective Emotion Classification: Comparing Features Extraction Algorithm Classified by Multi-class Support Vector Machine

Diana¹,

Sabiq²

2016

IJCCE

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show abstract

“…Before getting to a description of PCA, this tutorial first introduces mathematical concepts that will be used in PCA. It covers standard deviation, covariance, and eigenvectors [13]. This background knowledge is meant to make the PCA section very straightforward, but can be skipped if the concepts are already familiar [10,14].…”

Section: Introductionmentioning

confidence: 99%

An Overview of Principal Component Analysis

Karamizadeh

Abdullah

Manaf

et al. 2013

JSIP

356

168

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The principal component analysis (PCA) is a kind of algorithms in biometrics. It is a statistics technical and used orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables. PCA also is a tool to reduce multidimensional data to lower dimensions while retaining most of the information. It covers standard deviation, covariance, and eigenvectors. This background knowledge is meant to make the PCA section very straightforward, but can be skipped if the concepts are already familiar.

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An Investigation of Gabor PCA and Different Similarity Measure Techniques for Image Classification

Hemavathi¹,

Anusha²,

Mahantesh³

et al. 2015

Advances in Intelligent Systems and Computing

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Comparison of PCA, LDA and Gabor Features for Face Recognition Using Fuzzy Neural Network

Cited by 9 publications

References 13 publications

Cognitive-Affective Emotion Classification: Comparing Features Extraction Algorithm Classified by Multi-class Support Vector Machine

Cognitive-Affective Emotion Classification: Comparing Features Extraction Algorithm Classified by Multi-class Support Vector Machine

An Overview of Principal Component Analysis

An Investigation of Gabor PCA and Different Similarity Measure Techniques for Image Classification

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