Component-Based Cascade Linear Discriminant Analysis for Face Recognition

Zhang, Wenchao; Shan, Shiguang; Gao, Wen; Chang, Yizheng; Cao, Bo

doi:10.1007/978-3-540-30548-4_33

Cited by 16 publications

(6 citation statements)

References 15 publications

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“…Some recent advances in PCA-based algorithms include multi-linear subspace analysis [90], symmetrical PCA [91], two-dimensional PCA [92,93], eigenbands [94], adaptively weighted subpattern PCA [95], weighted modular PCA [96], Kernel PCA [97,98] and diagonal PCA [99]. Examples of recent LDA-based algorithms include Direct LDA [100,101], Direct-weighted LDA [102], Nullspace LDA [103,104], Dual-space LDA [105], Pair-wise LDA [106], Regularized Discriminant Analysis [107], Generalized Singular Value Decomposition [108,109], Direct Fractional-Step LDA [110], Boosting LDA [111], Discriminant Local Feature Analysis [112], Kernel PCA/LDA [113,114], Kernel Scatter-Difference-based Discriminant Analysis [115], 2D-LDA [116,117], Fourier-LDA [118], Gabor-LDA [119], Block LDA [120], Enhanced FLD [121], Component-based Cascade LDA [122], and incremental LDA [123], to name but a few. All these methods purportedly obtain better recognition results than the baseline techniques.…”

Section: Statisticalmentioning

confidence: 99%

A Survey of Face Recognition Techniques

Jafri¹,

Arabnia²

2009

Journal of Information Processing Systems

563

269

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Face recognition presents a challenging problem in the field of image analysis and computer vision, and as such has received a great deal of attention over the last few years because of its many applications in various domains. Face recognition techniques can be broadly divided into three categories based on the face data acquisition methodology: methods that operate on intensity images; those that deal with video sequences; and those that require other sensory data such as 3D information or infra-red imagery. In this paper, an overview of some of the well-known methods in each of these categories is provided and some of the benefits and drawbacks of the schemes mentioned therein are examined. Furthermore, a discussion outlining the incentive for using face recognition, the applications of this technology, and some of the difficulties plaguing current systems with regard to this task has also been provided. This paper also mentions some of the most recent algorithms developed for this purpose and attempts to give an idea of the state of the art of face recognition technology.

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Section: Statisticalmentioning

confidence: 99%

A Survey of Face Recognition Techniques

Jafri¹,

Arabnia²

2009

Journal of Information Processing Systems

563

269

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“…Various holistic approaches used for face recognition, such as Eigenfaces [10], Fisherfaces [11], Independent Component Analysis (ICA) [12], Moment Invariants [13], Discrete Cosine Transform (DCT) [14], etc. Moreover, component feature-based techniques are described by facial components with various ideas, for examples, components with support vector machine (SVM) [15], LDA [16], 3D models [17], etc.…”

Section: Introductionmentioning

confidence: 99%

Component-based face recognition using statistical pattern matching analysis

Paul

Bouakaz

Rahman

et al. 2020

Pattern Anal Applic

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The aim of this research is to develop a fusion concept to component-based face recognition algorithms for features analysis of binary facial components (BFCs), which are invariant to illumination, expression, pose variations and partial occlusion. To analyze the features, using statistical pattern matching concepts, which are the combination of Chi-square (CSQ), Hu moment invariants (HuMIs), absolute difference probability of white pixels (AbsDifPWPs) and geometric distance values (GDVs) have been proposed for face recognition. The individual grayscale face image is cropped by applying the Viola-Jones face detection algorithm from a face database having variations in illumination, appearance, pose and partial occlusion with complex backgrounds. Doing illumination correction through histogram linearization technique, the grayscale face components such as eyes, nose and mouth regions are extracted using the 2D geometric positions. The binary face image is created by applying cumulative probability distribution function with Otsu adaptive thresholding method and then extracted BFCs such as eyes, nose and mouth regions. Five statistical pattern matching tools such as the standard deviation of CSQ values with probability of white pixels (PWPs), standard deviation of HuMIs with Hu's seven moment invariants, Abs-DifPWPs and GDVs are developed for recognition purpose. GDVs are determined between two similar facial corner points (FCPs) and nine FCPs are extracted from binary whole face and BFCs. Pixel Intensity Values (PIVs) which are determined using L 2 norms from grayscale values of the whole face and grayscale values of the face components. Experiment is performed using BioID Face Database on the basis of these pattern matching tools and appropriate threshold values with logical and conditional operators and gives the best expected results from true positive rate perspective. Keywords Binary facial component Á Histogram linearization technique Á Otsu thresholding Á Probability of white pixels Á Hu's moment invariants Á Facial corner points

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“…In order to improve the ability of classification and recognition, many supervised methods such as Marginal Fisher Analysis (MFA) [10] and Local Discriminant Embedding (LDE) [11] have been developed. In contrast to above mentioned methods which directly use whole face images as the input patterns, some subpattern-based techniques have been also proposed [12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

A discrimination preserving projection approach for face recognition

Sun

Feng

et al. 2012

Proceedings of 2012 2nd International Conference on Computer Science and Network Technology

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Linear subspace learning has achieved great success in feature extraction, and it aims to map high dimensional data into low dimensional feature space which can reflect the important inherent structure of original data. In this paper, a novel approach termed Discrimination Preserving Projection (DPP) based on Sparse coding is proposed, which mainly focus on combining locality supervised linear subspace learning with sparse coding. In our approach, we decompose images into two parts including more discrimination part and less discrimination part via dictionary learning and sparse coding firstly. Then, a locality supervised criterion which preserves the more discrimination part components while weaken the less discrimination part components is presented. Extensive experiments on publicly available databases are conducted to verify the effectiveness of the proposed algorithm and corroborate the above claims.

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Component-Based Cascade Linear Discriminant Analysis for Face Recognition

Cited by 16 publications

References 15 publications

A Survey of Face Recognition Techniques

A Survey of Face Recognition Techniques

Component-based face recognition using statistical pattern matching analysis

A discrimination preserving projection approach for face recognition

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