2014
DOI: 10.1016/j.asoc.2014.05.007
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Face recognition using transform domain feature extraction and PSO-based feature selection

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Cited by 99 publications
(39 citation statements)
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“…Recognition rate ThBPSO [17] 95.00 SLGS [14] 78.00 KLDA [15] 92.92 NNRW [18] 89.80 2D-NNRW [16] 91.90 The proposed method 97.50 …”
Section: Methodsmentioning
confidence: 99%
“…Recognition rate ThBPSO [17] 95.00 SLGS [14] 78.00 KLDA [15] 92.92 NNRW [18] 89.80 2D-NNRW [16] 91.90 The proposed method 97.50 …”
Section: Methodsmentioning
confidence: 99%
“…In [22], PSO has been incorporated into AdaBoost to improve recognition accuracy. In addition, PSO is used to select features extracted from Discrete Fourier Transform, Discrete Wavelet Transform and Discrete Cosine Transform in [23].…”
Section: Feature Selectionmentioning
confidence: 99%
“…From the literature review [20][21][22][23], branch and bound, sequential selection, mutual information (MI), Minimum Redundancy Maximum Relevance (mRMR), and evolutionary approaches such as Particle Swarm Optimization (PSO), have been implemented for optimized feature selection. The branch and bound method adopts monotonicity assumptions when searching for the optimal feature subset, whereas sequential selection that adds or removes one feature at a time is computationally expensive [20].…”
Section: Feature Selectionmentioning
confidence: 99%
“…Due to some attractive aspects such as quick convergence and easy implementation, PSO has been broadly applied for different optimization problems [23][24][25][26][27][28][29][30][49][50][51][52]. A set of randomly generated solutions propagates in the whole search space toward the optimal solution over a number of iterations by sharing information between all particles.…”
Section: Pso Algorithmmentioning
confidence: 99%