Kernel based locality – Sensitive discriminative sparse representation for face recognition

Benuwa, Ben‐Bright; Ghansah, Benjamin; Ansah, Ernest K.

doi:10.1016/j.sciaf.2019.e00249

Cited by 5 publications

(5 citation statements)

References 27 publications

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“…The classification accuracies of all methods are summarized in Table 3. Besides the algorithms already mentioned in the above experiments, we also add five state-of-the-art algorithms for comparison, which are EasyDL [45], MCDL [46], IC-DDL [47], SADL [48] and K-LSDSR [49]. We find that the analysis kernel KSVD algorithm can achieve the 96.8% classification accuracy, which shows that our method is competitive among all compared methods.…”

Section: E-yaleb Datasetmentioning

confidence: 89%

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Nonlinear Kernel Dictionary Learning Algorithm Based on Analysis Sparse Model

Miao

Zhang

2020

IEEE Access

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Section: E-yaleb Datasetmentioning

confidence: 89%

“…Just like the above mentioned two datasets, we also use the analysis KSVD algorithm for the classification experiment on [39] 91.9% KSVD 93.1% LC-KSVD [41] 94.5% EasyDL [45] 96.2% MCDL [46] 95.8% IC-DDL [47] 95.6% SADL [48] 94.9% kernel PCA 88.1% kernel KSVD 91.8% LKDL [41] 96.1% K-LSDSR [49] 96.8% analysis kernel KSVD 96.8%…”

Section: E-yaleb Datasetmentioning

confidence: 99%

Nonlinear Kernel Dictionary Learning Algorithm Based on Analysis Sparse Model

Miao

Zhang

2020

IEEE Access

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“…Applied Computational Intelligence and Soft Computing Local nonlinear multilayer contrast patterns (LNLMCP) 97.50 YaleB [35] Discriminative sparse representation via l2 regularization 82.61 YaleB [32] GLRAM 97.25 AT&T [33] Fisher discriminative dictionary learning (FDDL) 96.7 AT&T [31] PSO-KNN 98.75 AT&T [31] PCA-LDA fusion algorithm 98.00 AT&T [35] Discriminative sparse representation via l2 regularization 95.00 AT&T PSO + EDU and PSO + KNN trials. Subsequently, the ABC and GA meta-heuristic algorithms produced a similar result to PSO, but PSO is computationally less expensive than both.…”

Section: Discussionmentioning

confidence: 99%

Analysis and Implementation of Optimization Techniques for Facial Recognition

Appati

Abu

Owusu

et al. 2021

Applied Computational Intelligence and Soft Computing

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Amidst the wide spectrum of recognition methods proposed, there is still the challenge of these algorithms not yielding optimal accuracy against illumination, pose, and facial expression. In recent years, considerable attention has been on the use of swarm intelligence methods to help resolve some of these persistent issues. In this study, the principal component analysis (PCA) method with the inherent property of dimensionality reduction was adopted for feature selection. The resultant features were optimized using the particle swarm optimization (PSO) algorithm. For the purpose of performance comparison, the resultant features were also optimized with the genetic algorithm (GA) and the artificial bee colony (ABC). The optimized features were used for the recognition using Euclidean distance (EUD), K-nearest neighbor (KNN), and the support vector machine (SVM) as classifiers. Experimental results of these hybrid models on the ORL dataset reveal an accuracy of 99.25% for PSO and KNN, followed by ABC with 93.72% and GA with 87.50%. On the central, an experimentation of the PSO, GA, and ABC on the YaleB dataset results in 100% accuracy demonstrating their efficiencies over the state-of-the art methods.

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“…The sparse approximation based methods are outperforming existing techniques constrained to limited training data in terms of classification accuracy and easier implementation [14, 15, 16]. In the last decade, many sparse approximation based methods have evolved that performs very well [17, 18, 19]. In these methods, the test vectors are approximated to linear sparse combination of training vectors and final matching contribution is calculated for further classification of test vector.…”

Section: Literature Surveymentioning

confidence: 99%

Real Time Face Recognition with limited training data: Feature Transfer Learning integrating CNN and Sparse Approximation

Bajpai

Mishra

2021

Preprint

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It is highly challenging to obtain high performance with limited and unconstrained data in real time face recognition applications. Sparse Approximation is a fast and computationally efficient method for the above application as it requires no training time as compared to deep learning methods. It eliminates the training time by assuming that the test image can be approximated by the sum of individual contributions of the training images from different classes and the class with maximum contribution is closest to the test image. The efficiency of the Sparse Approximation method can be further increased by providing high quality features as input for classification. Hence, we propose to integrate pre-trained CNN architecture to extract the highly discriminative features from the image dataset for Sparse classification. The proposed approach provides better performance even for one training image per class in complex environment as compared to the existing methods. Highlight of the present approach is the results obtained for LFW dataset with one and thirteen training images per class are 84.86% and 96.14% respectively, whereas the existing deep learning methods use a large amount of training data to achieve comparable results.

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Kernel based locality – Sensitive discriminative sparse representation for face recognition

Cited by 5 publications

References 27 publications

Nonlinear Kernel Dictionary Learning Algorithm Based on Analysis Sparse Model

Nonlinear Kernel Dictionary Learning Algorithm Based on Analysis Sparse Model

Analysis and Implementation of Optimization Techniques for Facial Recognition

Real Time Face Recognition with limited training data: Feature Transfer Learning integrating CNN and Sparse Approximation

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