A Comprehensive Survey of Feature Extraction and Feature Selection Techniques of Face Recognition System

Saini, S. S.; Malhotra, Preeti

doi:10.4108/eai.16-5-2020.2303968

Cited by 2 publications

(5 citation statements)

References 28 publications

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“…In order to evaluate DB2DPCA, we compare its performances in image reconstruction and recognition with four related algorithms, i.e., PCA, 2DPCA, A2DPCA and (2D) 2 PCA, on four image databases. To further exploit the properties of DB2DPCA, we additionally conduct image recognition experiments when the pixels in images are reordered by certain rules and when images are reshaped to different sizes.…”

Section: Methodsmentioning

confidence: 99%

“…By comparing between the two bilateral algorithms, DB2DPCA significantly outperforms (2D) 2 PCA in all experiments. The main reason is that (2D) 2 PCA projects the rows and columns of images onto two lateral eigenvectors simultaneously, thus mixing bilateral information together and leading to information loss.…”

Section: Other Comparisonsmentioning

confidence: 98%

“…In the field of machine learning and pattern recognition, feature extraction from massive high-dimensional data is a very demanding job [1]. Various applications of feature extraction methods include face recognition [2], EEG-based emotion recognition [3], rice disease recognition [4], facial emotion recognition [5], speaker recognition [6] and diagnosis of COVID-19 related diseases [7,8].…”

Section: Introductionmentioning

confidence: 99%

“…Following this idea, (2D) 2 PCA [21], generalized 2DPCA (G2DPCA) [22] and sequential row-column 2DPCA (RC2DPCA) [23] were proposed in order to fuse the bilateral information of images. (2D) 2 PCA calculates bilateral eigenvectors by directly taking the projection matrices from 2DPCA and A2DPCA. Generalized 2DPCA calculates bilateral eigenvectors by iteratively solving a minimization problem.…”

Section: Introductionmentioning

confidence: 99%

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Fusion of Bilateral 2DPCA Information for Image Reconstruction and Recognition

et al. 2022

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Being an efficient image reconstruction and recognition algorithm, two-dimensional PCA (2DPCA) has an obvious disadvantage in that it treats the rows and columns of images unequally. To exploit the other lateral information of images, alternative 2DPCA (A2DPCA) and a series of bilateral 2DPCA algorithms have been proposed. This paper proposes a new algorithm named direct bilateral 2DPCA (DB2DPCA) by fusing bilateral information from images directly—that is, we concatenate the projection results of 2DPCA and A2DPCA together as the projection result of DB2DPCA and we average between the reconstruction results of 2DPCA and A2DPCA as the reconstruction result of DB2DPCA. The relationships between DB2DPCA and related algorithms are discussed under some extreme conditions when images are reshaped. To test the proposed algorithm, we conduct experiments of image reconstruction and recognition on two face databases, a handwritten character database and a palmprint database. The performances of different algorithms are evaluated by reconstruction errors and classification accuracies. Experimental results show that DB2DPCA generally outperforms competing algorithms both in image reconstruction and recognition. Additional experiments on reordered and reshaped databases further demonstrate the superiority of the proposed algorithm. In conclusion, DB2DPCA is a rather simple but highly effective algorithm for image reconstruction and recognition.

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Section: Methodsmentioning

confidence: 99%

Section: Other Comparisonsmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Fusion of Bilateral 2DPCA Information for Image Reconstruction and Recognition

et al. 2022

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“…The effectiveness of a face recognition system is mainly dependent on the feature extraction and selection phase [1]. Besides, evolutionary algorithms like neural networks, and fuzzy logic, were also incorporated into the face recognition systems to increase the recognition rate.…”

Section: Introductionmentioning

confidence: 99%

Combining Color Texture and Shape Features In a Multi-Input Convolutional Neural Network for Efficient Face Recognition In Unconstrained Environments

Bahroun

2023

Preprint

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Developing an effective feature representation for face recognition in an un-constrained environment is still a challenging research field. However, in that environment, face images suffer from several issues that could affect the achieved results such as facial expression, occlusion, low resolution, noise, illumination and pose variation. In this paper, an end-to-end multi-input deep convolu-tional neural network architecture was designed for face recognition. The newly designed CNN structure can take full advantage of multi-modal 2D/3D features extracted from face such as: 3DMM-Mesh LBP image, LBPC image, and the face image itself. Through experiments conducted on the LFW, YTF, Multi-PIE, and Bosphorus databases, the obtained results showed a significant improvement compared to traditional state-of-the-art methods which demonstrated the strength of introducing a multi-input convolutional neural network for face recognition in uncontrolled environments.

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A Comprehensive Survey of Feature Extraction and Feature Selection Techniques of Face Recognition System

Cited by 2 publications

References 28 publications

Fusion of Bilateral 2DPCA Information for Image Reconstruction and Recognition

Fusion of Bilateral 2DPCA Information for Image Reconstruction and Recognition

Combining Color Texture and Shape Features In a Multi-Input Convolutional Neural Network for Efficient Face Recognition In Unconstrained Environments

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