Face recognition via semi-supervised discriminant local analysis

Ling, Goh Fan; Pang, Ying Han; Khor, Ean Teng; Yin, Ooi Shih

doi:10.1109/icsipa.2015.7412207

Cited by 5 publications

(1 citation statement)

References 26 publications

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“…Researchers have created many dimensionality reduction methods such as PCA, LDA, marginal Fisher analysis (MFA), maximum margin criterion (MMC) [35], locality preserving projections (LPP) [36], Sparsity Preserving Projection (SSP) [33], semi-supervised dimensionality reduction (SSDR), semi-supervised discriminant analysis (SDA) [37] and random projection (RP) [38].…”

Section: Dimensionality Reduction Using Random Projectionmentioning

confidence: 99%

Group Class Residual ℓ1-Minimization on Random Projection Sparse Representation Classifier for Face Recognition

et al. 2022

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Sparse Representation-based Classification (SRC) has been seen to be a reliable Face Recognition technique. The ℓ1 Bayesian based on the Lasso algorithm has proven to be most effective in class identification and computation complexity. In this paper, we revisit classification algorithm and then recommend the group-based classification. The proposed modified algorithm, which is called as Group Class Residual Sparse Representation-based Classification (GCR-SRC), extends the coherency of the test sample to the whole training samples of the identified class rather than only to the nearest one of the training samples. Our method is based on the nearest coherency between a test sample and the identified training samples. To reduce the dimension of the training samples, we choose random projection for feature extraction. This method is selected to reduce the computational cost without increasing the algorithm’s complexity. From the simulation result, the reduction factor (ρ) 64 can achieve a maximum recognition rate about 10% higher than the SRC original using the downscaling method. Our proposed method’s feasibility and effectiveness are tested on four popular face databases, namely AT&T, Yale B, Georgia Tech, and AR Dataset. GCR-SRC and GCR-RP-SRC achieved up to 4% more accurate than SRC random projection with class-specific residuals. The experiment results show that the face recognition technology based on random projection and group-class-based not only reduces the dimension of the face data but also increases the recognition accuracy, indicating that it is a feasible method for face recognition.

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