Eigenfaces, Fisherfaces, Laplacianfaces, Marginfaces – How to Face the Face Verification Task

Smiatacz, Maciej

doi:10.1007/978-3-319-00969-8_18

Cited by 12 publications

(7 citation statements)

References 8 publications

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“…In this section, we conducted a set of experiments to verify the effectiveness of the proposed classifier under the different feature extraction condition in 40x48 pixels database. We use some mainstream feature extraction methods such as Eigenfaces [48], Neighborhood Preserving Embedding (NPE) [12] and Local Binary Patterns (LBP) [14]. Considering SVM has better performance than other common classifiers, we compare the proposed IT2T-SFCS with SVM, which is employed for classification under the above feature extraction condition.…”

Section: Comparisons With Other Classification Methodsmentioning

confidence: 99%

An interval type-2 T-S fuzzy classification system based on PSO and SVM for gender recognition

Chen

et al. 2014

Multimed Tools Appl

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In this paper, an interval type-2 Takagi-Sugeno fuzzy classification system (IT2T-SFCS) learned by particle swarm optimization (PSO) and support vector machine (SVM) for antecedent and consequent parameters optimization is proposed. The IT2T-SFCS is constructed by fuzzy if-then rules whose antecedents are interval type-2 fuzzy sets and consequents are linear state equations. The antecedents of IT2T-SFCS use the fuzzy iterative self-organizing data analysis technique (ISODATA) and PSO to learn and calculate the optimal centers and the uncertain widths of the Gaussian membership functions. Consequent parameters in IT2T-SFCS are learned through SVM for the purpose of achieving higher generalization ability. The proposed IT2T-SFCS is able to directly handle uncertainties, minimize the effects of uncertainties and get the better generalization performance, which inherits the benefits of interval type-2 T-S fuzzy system and SVM. For demonstration, IT2T-SFCS is used as a classifier in gender recognition. The experimental results show that the performance of the proposed IT2T-SFCS is superior to that of the previous mainstream classifiers.

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Section: Comparisons With Other Classification Methodsmentioning

confidence: 99%

An interval type-2 T-S fuzzy classification system based on PSO and SVM for gender recognition

Chen

et al. 2014

Multimed Tools Appl

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“…These principle components keep most of the facial features. Finally, the eigenface technique projects the mean-shifted images into the eigenspace, using the principal eigenvectors [1,[3][4][5].…”

Section: Eigenface Techniquementioning

confidence: 99%

“…Before the detection process, the input image need to be normalized. This processed can be split into four steps for a given unknown image Γ [1,3,4].…”

Section: Eigenface Techniquementioning

confidence: 99%

Image based Cats and Possums Identification for Intelligent Trapping Systems

Perera¹,

Collins²

2017

IJCA

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Eigenface based recognition is widely used to identify individual faces. This technique is commonly used in human recognition domain. In this research, we are investigating for implementation of a possible solution; which can be used to identify species. The eigenface technique is a novel approach in animal recognition domain [1,2]. In this application, cats and possums are targeted as species of interest. The main reason for targeting cats and possum is, these introduced species are classified as threat to New Zealand natural environment and native wildlife. The next phase of this research project is to implement a vision based trapping system. This paper explains a novel classifier to use with eigenface techniques to separate different species.

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“…These principle components keep most of the facial features. Finally the eigenface technique projects the mean-shifted images into the eigenspace, using the principal eigenvectors, those eigenvectors with the largest eigenvalues [6,11,12].…”

Section: Eigenface Techniquementioning

confidence: 99%

Imaged based species recognition system

Perera

Collins

2015

2015 9th International Conference on Sensing Technology (ICST)

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Animal species recognition is one area in which a limited amount of research has been undertaken. Especially in the pest control domain, basic primitive technologies are still used, such as identifying species in a tracking tunnel by capturing ink footprints on paper. These traps only differentiate various species by varying the baits used in the trap. This is not a reliable method.The main aim of this research is to develop a species recognition system for traps. This system uses an eigenface based identification technique. This imaged based sensing application is focused on feral rodents such as possums, cats and weasels.Traditionally, the eigenface technique is used in the human face recognition domain. This technique is novel in animal species recognition domain. When the technique was applied to animal images with different backgrounds, it had 55%, 33% and 45% successful detection rates for possums, cats and weasels respectively. Once the background is removed from the training images the detection rate increased significantly to 65%, 52% and 64% respectively.

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Eigenfaces, Fisherfaces, Laplacianfaces, Marginfaces – How to Face the Face Verification Task

Cited by 12 publications

References 8 publications

An interval type-2 T-S fuzzy classification system based on PSO and SVM for gender recognition

An interval type-2 T-S fuzzy classification system based on PSO and SVM for gender recognition

Image based Cats and Possums Identification for Intelligent Trapping Systems

Imaged based species recognition system

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