Determination of Positional Characteristics of Numbers in the Residual Class System

Krasnobayev, Victor; Кuznetsov, Alexandr; Popenko, Victoria; Kononchenko, Anna; Кузнецова, Тетяна

doi:10.1109/dessert50317.2020.9125030

Cited by 3 publications

(1 citation statement)

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“…Wei et al [28] introduces a new probability judgement method for sparse representation classifiers (SRCs), which employs a probability judgement model rather than directly employing the truncation residual. Krasnobayev et al [29] proposed the method for the determination of the rank of a number in the system of residual classes. SRC uses a decision rule based on class reconstruction residuals, which is then used by Yang et al [30] as a criterion to direct the development of a feature extraction strategy called SRC steered discriminative projection (SRC-DP).…”

Section: Literature Reviewmentioning

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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Section: Literature Reviewmentioning

confidence: 99%