Aggregation of classifiers based on image transformations in biometric face recognition

Jarillo, Gabriel; Pedrycz, Witold; Reformat, Marek

doi:10.1007/s00138-007-0088-9

Cited by 11 publications

(4 citation statements)

References 32 publications

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“…Until now, a large number of face recognition methods, such as appearance-based methods [1][2][3][4], Gabor wavelet methods [5][6][7]35,38], and machine learning-based methods [8,9], have been developed for still images. Zhao et al [10] provided a comprehensive survey of the studies of machine recognition of faces, categorized existing recognition techniques, and presented a detailed description of a number of representative methods.…”

Section: Introductionmentioning

confidence: 99%

Face recognition using decimated redundant discrete wavelet transforms

Tang

Pedrycz

2011

Machine Vision and Applications

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As discrete wavelet transform (DWT) is sensitive to the translation/shift of input signals, its effectiveness could be lessened for face recognition, particularly when the face images are translated. To alleviate drawbacks resulted from this translation effect, we propose a decimated redundant DWT (DRDWT)-based face recognition method, where the decimation-based DWTs are performed on the original signal and its 1-stepshift, respectively. Even though the DRDWT realizes the decimation, it enables us to explore the translation invariant DWT representation for the periodic shifts of the probe image that is the most similar to the gallery images. Therefore, it can solve the problem of translation sensitivity of the original DWT and address the translation effect occurring between the probe image and the gallery image. To further improve the recognition performance, we combine the global wavelet features obtained from the entire face and the local wavelet features obtained from face patches to represent both holistic and detail facial features, apply separate classifiers to global and local features and combine the resulted global and local classifiers to form an ensemble classifier. Experimental results reported for the FERET and FRGCv2.0

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Section: Introductionmentioning

confidence: 99%

Face recognition using decimated redundant discrete wavelet transforms

Tang

Pedrycz

2011

Machine Vision and Applications

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“…Kernel-PCA Schölkopf et al [13] have developed a nonlinear PCA called Kernel-PCA. The kernel-PCA is not interested in principal components in input space, but rather in principal components of variables which are nonlinearly related to the input variables [22]. They compute PCA in another dot product feature space F, which is related to the input space R MN by a possibly nonlinear map…”

Section: Color Space Modelsmentioning

confidence: 99%

Kernel-PCA for face recognition in different color spaces

Ebied

2012

2012 Seventh International Conference on Computer Engineering &Amp; Systems (ICCES)

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Different color spaces are better for different applications. This paper investigates the performance of face recognition with some color spaces using kernel-based Principal Component Analysis (Kernel-PCA). Kernel-PCA is a non-linear extension from the popular algorithm PCA. Experiments are performed with the Gaussian kernel function. Color spaces are linear or non-linear transform from RGB. In this paper, the RGB, YCbCr, and HSV color spaces are compared with the gray image (luminance information Y). Kernel-PCA is used to extract features from individual color components or from combining the three components of every color space in one vector. The experiments are performed on FEI color database. FEI database is frontal face images with seven profile images rotation of up to about 180 degrees and two different facial expression images. The experimental results show that the V color component of the HSV color space outperform all the used color organization.

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“…Classifiers based on the RBF neural networks are fused through the majority rule in [15]. In [19], majority voting and Bayesian product are used to aggregate chosen methods of dimensionality reduction. The work [28] at the decision level suggests the weighted sum rule for fusion of similarity matrices.…”

Section: Introductionmentioning

confidence: 99%

Generalized Choquet Integral for Face Recognition

Karczmarek

Kiersztyn

Pedrycz

2017

Int. J. Fuzzy Syst.

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In this study, we introduce a recent multicriteria decision theory concept of a new, generalized form of Choquet integral function and its application, in particular to the problem of face classification based on the aggregation of classifiers. Such function may be constructed by a simple replacement of the product used under the Choquet integral sign by any t-norm. This idea brings forward a broad class of aggregation operators, which can be incorporated into the decision-making theory. In this context, in a series of experiments we compare the most known t-norms and thoroughly examine their performance in the process of combining individual classifiers based either on facial regions or classic face recognition methods. Such kind of generalization can successfully improve the classification process provided that the parameters of the t-norms are carefully adjusted.

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Aggregation of classifiers based on image transformations in biometric face recognition

Cited by 11 publications

References 32 publications

Face recognition using decimated redundant discrete wavelet transforms

Face recognition using decimated redundant discrete wavelet transforms

Kernel-PCA for face recognition in different color spaces

Generalized Choquet Integral for Face Recognition

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