Two-dimensional color uncorrelated discriminant analysis for face recognition

Zhao, Cairong; Miao, Duoqian; Zhang, Lai; Gao, Chengde; Liu, Chuancai; Yang, Jingyu

doi:10.1016/j.neucom.2013.01.021

Cited by 29 publications

(20 citation statements)

References 43 publications

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“…There are two approaches to feature selection: wrappers, which use the learning and classification algorithms, and filters, which do not use any learning methods. In order to improve classification accuracy and reduce feature vector dimension, multi-class Fisher's linear discriminant analysis (FLDA) was used for feature selection in this study [15,16,27]. According to FLDA, the discriminative feature should maximize between-class separability and should minimize the withinclass variability.…”

Section: Feature Selectionmentioning

confidence: 99%

Use of wavelet-based two-dimensional scaling moments and structural features in cascade neuro-fuzzy classifiers for handwritten digit recognition

Çetişli

Edizkan

2014

Neural Comput & Applic

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In this paper, a novel handwritten digit recognition system is proposed. The system consist of feature extraction, feature selection and classification stages. The features of digits are extracted by using the moment-based and structural-based methods. For the moment-based method, wavelet-based two-dimensional scaling moments (2-DSMs), which have uniquely different angular divisions of polar form, are considered. The structural-based features including profiles, intersections of horizontal and vertical straight lines, concavity, number and location of holes are used. In the feature selection stage, Fisher's linear discriminant analysis is used to obtain the discriminative features. The feature selection is performed to improve not only the processing time but also recognition rates. In the classification stage, the digits are classified by neuro-fuzzy classifiers (NFCs). A three-stage cascade NFCs with rejection strategy is used in the system to improve the misclassification rate for the handwritten digit recognition task. The experiments are performed on the MNIST and USPS handwritten digit databases. The high correct classification rates of 98.72 % for MNIST and 97.21 % for USPS are attained by using only one hundred robust hybrid features and cascade NFCs. The experiments showed that the proposed system yields better results among those systems that use only moment-based features.

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Section: Feature Selectionmentioning

confidence: 99%

Use of wavelet-based two-dimensional scaling moments and structural features in cascade neuro-fuzzy classifiers for handwritten digit recognition

Çetişli

Edizkan

2014

Neural Comput & Applic

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“…Then it concatenates the extracted features from color components to do classification. Motivated by the uncorrelated linear discriminant analysis (ULDA) [35], statistically orthogonal analysis (SOA) [36] and two-dimensional color uncorrelated discriminant analysis (2DCUDA) [37] in turn extract discriminant features of R, G and B components and simultaneously make the achieved projective transformations mutually statistically orthogonal.…”

Section: Introductionmentioning

confidence: 99%

Uncorrelated multi-set feature learning for color face recognition

Jing

Dong

et al. 2016

Pattern Recognition

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“…Based on the PCA model, some improved methods have been presented including 2D PCA [2,3], incremental PCA [4], block PCA [5,6], etc. Moreover, many methods using matrix decomposition and linear combination have become very popular, such as linear discriminant analysis (LDA) [7][8][9][10][11], independent component analysis (ICA) [12][13][14][15][16], singular value decomposition (SVD) [17][18][19], discrete wavelet transform (DWT) [20,21], etc. Also, a method called k-LDA, which combines LDA with PCA, is proposed to process image classification [22].…”

Section: Introductionmentioning

confidence: 99%

A Novel Texture Feature Description Method Based on the Generalized Gabor Direction Pattern and Weighted Discrepancy Measurement Model

et al. 2016

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Abstract:Texture feature description is a remarkable challenge in the fields of computer vision and pattern recognition. Since the traditional texture feature description method, the local binary pattern (LBP), is unable to acquire more detailed direction information and always sensitive to noise, we propose a novel method based on generalized Gabor direction pattern (GGDP) and weighted discrepancy measurement model (WDMM) to overcome those defects. Firstly, a novel patch-structure direction pattern (PDP) is proposed, which can extract rich feature information and be insensitive to noise. Then, motivated by searching for a description method that can explore richer and more discriminant texture features and reducing the local Gabor feature vector's high dimension problem, we extend PDP to form the GGDP method with multi-channel Gabor space. Furthermore, WDMM, which can effectively measure the feature distance between two images, is presented for the classification and recognition of image samples. Simulated experiments on olivetti research laboratory (ORL), Carnegie Mellon University pose, illumination, and expression (CMUPIE) and Yale B face databases under different illumination or facial expression conditions indicate that the proposed method outperforms other existing classical methods.

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Two-dimensional color uncorrelated discriminant analysis for face recognition

Cited by 29 publications

References 43 publications

Use of wavelet-based two-dimensional scaling moments and structural features in cascade neuro-fuzzy classifiers for handwritten digit recognition

Use of wavelet-based two-dimensional scaling moments and structural features in cascade neuro-fuzzy classifiers for handwritten digit recognition

Uncorrelated multi-set feature learning for color face recognition

A Novel Texture Feature Description Method Based on the Generalized Gabor Direction Pattern and Weighted Discrepancy Measurement Model

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