Color image canonical correlation analysis for face feature extraction and recognition

Jing, Xiao‐Yuan; Sheng, Li; Lan, Chao; Zhang, David; Yang, Jingyu; Liu, Qian

doi:10.1016/j.sigpro.2011.02.016

Cited by 46 publications

(22 citation statements)

References 19 publications

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“…Recently, there has been increased interest in the use of CCA for feature fusion in various pattern recognition applications [10][11][12][13][14][15][16][17], in [10], CCA is applied to feature fusion and image recognition for the first time. The method using correlation feature of two groups of feature can find more effective discriminate information.…”

Section: Related Workmentioning

confidence: 99%

“…A multiset integrated canonical correlation analysis(MICCA) is presented in [12], it extracts multiple features from the same patterns by using different feature extraction methods and finally expresses the integral correlation among multi-group features using MICCA to remove the redundancy between features. In [13], a color image CCA (CICCA) is proposed, which can extract three color components and provide the analytical solution. It means the method can directly acquire the typically correlative features of three input datasets.…”

Section: Related Workmentioning

confidence: 99%

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Application to Three-Dimensional Canonical Correlation Analysis for Feature Fusion in Image Recognition

Gong¹,

Zhou²,

Wu³

et al. 2011

JCP

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This paper presents a three-dimensional canonical correlation analysis (TCCA) method, and applies it to feature fusion for image recognition. It is an extension of traditional canonical correlation analysis (CCA) and two-dimensional canonical correlation analysis (2DCCA). Considering two views of a three-dimensional data, the TCCA can directly find the relations between them without reshaping the data into matrices or vectors, We stress that TCCA dramatically reduce the computational complexity, compared to the CCA and 2DCCA. To evaluate the algorithm, we are using Gabor wavelet to generate the three-dimensional data, and fusing them at the feature level by TCCA. Some experiments on ORL database and JAFEE database and compared with other methods, the results show that the TCCA not only the computing complexity is lower, the recognition performance is better but also suitable for data fusion. <br />

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Application to Three-Dimensional Canonical Correlation Analysis for Feature Fusion in Image Recognition

Gong¹,

Zhou²,

Wu³

et al. 2011

JCP

View full text Add to dashboard Cite

show abstract

“…In the specified period, the correlation coefficient and the correlation function between the signal ) (t x and ) (t y were defined as [10][11][12]:…”

Section: B Linear Correlation Analysismentioning

confidence: 99%

A Real Time Multi-Scale Correlation Analysis Method and its Application on the Data Mining in Thermodynamic Process

Wang

Liu

2011

2011 Seventh International Conference on Computational Intelligence and Security

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There is a complicated mapping between the state and measurement signal in the complex thermodynamic system, direct mechanism analysis is very difficult. Using wavelet analysis idea for reference, simple filter bank was designed to decompose the signal by frequency domain. The correlation feature of signals in different frequency scale can be discovered. Using such method to analyze furnace pressure signal of boiler in thermal power plant, the positive correlation between the low-frequency component of furnace pressure signal and the medium-frequency component of fuel was revealed; and the negative correlation between the highfrequency component variance of furnace pressure signal and the low-frequency component of fuel was discovered. Through further mechanism analysis the conclusion is that: the furnace pressure can reflect the change of the furnace temperature in medium-frequency; the intensification of the furnace pressure and furnace temperature fluctuation in boiler under low load condition is cased by the nonlinearity of object itself.

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“…CCA can only deal with two-modal data, but the number of modalities corresponding to one same object is usually more than two in real-world applications. Aiming at three-modal image data, color image CCA (CICCA) [19] constructs a correlation coherent subspace learning model of three color components, and the optimization problem of the model is theoretically derived to solve three equations. The model with analytical solutions is a classical correlation coherent subspace learning model, and its effectiveness has been shown in realworld image recognition tasks.…”

Section: Introductionmentioning

confidence: 99%

Multi-Modal Subspace Fusion via Cauchy Multi-Set Canonical Correlations

Zhu

Peng

et al. 2020

IEEE Access

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Multi-set canonical correlation analysis (MCCA) is a famous multi-modal coherent subspace learning method. However, sample-based between-modal and within-modal covariance matrices of MCCA usually deviate from real covariance matrices due to noise information and limited sample size. The deviation will weaken the performance of MCCA, especially in image recognition. Aiming at this challenging issue, we correct singular values of sample covariance matrices with the employment of Cauchy estimate theory and further obtain Cauchy covariance matrices that are closer to real covariance matrices. On the basis of Cauchy covariance matrices, we develop a novel multi-modal subspace fusion method, i.e. Cauchy multi-set canonical correlations. By maximizing Cauchy correlations between different modalities and constraining Cauchy scatters of within-modal data, the method can learn a Cauchy coherent fusion subspace with well discriminative power from a few images. Experiment results have shown the effectiveness of the proposed method, promising to the aims of this research.

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Color image canonical correlation analysis for face feature extraction and recognition

Cited by 46 publications

References 19 publications

Application to Three-Dimensional Canonical Correlation Analysis for Feature Fusion in Image Recognition

Application to Three-Dimensional Canonical Correlation Analysis for Feature Fusion in Image Recognition

A Real Time Multi-Scale Correlation Analysis Method and its Application on the Data Mining in Thermodynamic Process

Multi-Modal Subspace Fusion via Cauchy Multi-Set Canonical Correlations

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