Frontal face authentication using discriminating grids with morphological feature vectors

Kotropoulos, Constantine; Tefas, Anastasios; Pitas, Ioannis

doi:10.1109/6046.825791

Cited by 65 publications

(86 citation statements)

References 34 publications

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“…In this thesis, the protocols are abbreviated as PROTO1 and PROTO2. In PROTO1, experiments were implemented by adopting the combination of the left-one-out and the rotation estimates i.e., a variant of the jack-knife (Kotropoulos et al, 2000;Goudelis et al, 2007). This protocol has been used in multimodal authentication.…”

Section: Verification Experimental Protocolsmentioning

confidence: 99%

Face Biometrics Based on Principal Component Analysis and Linear Discriminant Analysis

Chan

Salleh²,

Ting

2010

Journal of Computer Science

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Problem statement:In facial biometrics, face features are used as the required human traits for automatic recognition. Feature extracted from face images are significant for face biometrics system performance. Approach: In this thesis, a framework of facial biometric was designed based on two subspace methods i.e., Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). First, PCA is used for dimension reduction, where original face images are projected into lower-dimensional face representations. Second, LDA was proposed to provide a solution of better discriminant. Both PCA and LDA features were presented to Euclidean distance measurement which is conveniently used as a benchmark. The algorithms were evaluated in face identification and verification using a standard face database-AT and T and a locally collected database-CBE. Each database consists of 400 images and 320 images respectively. Results: LDA-based methods outperform PCA for both face identification and verification. For face identification, PCA achieves accuracy of 91.9% (AT and T) and 76.7% (CBE) while LDA 94.2% (AT and T) and 83.1% (CBE). For face verification, PCA achieves Equal Error Rate (EER) of 1.15% (AT and T), 7.3% (CBE) while LDA 0.78% (AT and T) and 5.81% (CBE). Conclusion/Recommendations: This study had proved that, when given sufficient training samples, LDA is able to provide better discriminant ability in feature extraction for face biometrics.

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Section: Verification Experimental Protocolsmentioning

confidence: 99%

Face Biometrics Based on Principal Component Analysis and Linear Discriminant Analysis

Chan

Salleh²,

Ting

2010

Journal of Computer Science

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“…In the standard EGM a 2D Gabor based filter bank has been used for image analysis. The output of multiscale morphological dilation-erosion operations is a nonlinear alternative of the Gabor filters for multiscale analysis and has been successfully used for facial image analysis [2]. At each graph node that is located at image coordinates x a jet j(x) is formed as:…”

Section: Elastic Graph Matchingmentioning

confidence: 99%

“…The use of linear discriminant techniques at the feature vectors for selecting the most discriminant features has been proposed in [1,2]. Several schemes that aim at weighting the graph nodes according to their discriminatory power have been proposed [2,3].…”

Section: Introductionmentioning

confidence: 99%

“…A variant of the standard EGM, the so-called morphological elastic graph matching (MEGM), has been proposed for frontal face verification [2]. In MEGM the Gabor analysis has been superseded by multiscale morphological dilationerosion by a scaled structuring function [2].…”

Section: Introductionmentioning

confidence: 99%

“…In MEGM the Gabor analysis has been superseded by multiscale morphological dilationerosion by a scaled structuring function [2].…”

Section: Introductionmentioning

confidence: 99%

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Exploiting discriminant information in elastic graph matching

Zafeiriou

Tefas

Pitas

2005

IEEE International Conference on Image Processing 2005

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In this paper, we investigate the use of discriminant techniques in the elastic graph matching (EGM) algorithm. First we use discriminant analysis in the feature vectors of the nodes in order to find the most discriminant features. The similarity measure for discriminant feature vectors and the node deformation are combined in a discriminant manner in order to form a local similarity measure between nodes. Moreover, the local similarity values at the nodes of the elastic graph, are weighted by coefficients that are also derived by some discriminant analysis in order to form a total similarity measure between faces. We illustrate the improvements in performance in frontal face verification using a modified multiscale morphological analysis.

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