Face Recognition Employing DMWT Followed by FastICA

Ahmed, Ammar; Mikhael, W.B.

doi:10.1007/s00034-017-0653-z

Cited by 14 publications

(11 citation statements)

References 40 publications

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“…We also use a wrapping discrete curvelet transform for face images in the non-occluded part of face images, and FastICA [20] for de-correlation and feature reduction. We refer to these processes as an FBB module.…”

Section: Frequency Domain Feature Extractionmentioning

confidence: 99%

Face Recognition With Masks Based on Spatial Fine-Grained Frequency Domain Broadening

Chen

Xie

et al. 2022

IEEE Access

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Along with social distancing, wearing masks is an effective method of preventing the transmission of COVID-19 in the ongoing pandemic. However, masks occlude a large number of facial features, preventing facial recognition. The recognition rate of existing methods may be significantly reduced by the presence of masks. In this paper, we propose a method to effectively solve the problem of the lack of facial feature information needed to perform facial recognition on people wearing masks. The proposed approach uses image super-resolution technology to perform image preprocessing along with a deep bilinear module to improve EfficientNet. It also combines feature enhancement with frequency domain broadening, fuses the spatial features and frequency domain features of the unoccluded areas of the face, and classifies the fused features. The features of the unoccluded area are increased to improve the accuracy of recognition of masked faces. The results of a cross-validation show that the proposed approach achieved an accuracy of 98% on the RMFRD dataset, as well as a higher recognition rate and faster speed than previous methods. In addition, we also performed an experimental evaluation in an actual facial recognition system and achieved an accuracy of 99%, which demonstrates the effectiveness and practicability of the proposed method.INDEX TERMS Face recognition with mask, convolutional neural network, frequency domain widening, bilinear module, RMFRD dataset.

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Section: Frequency Domain Feature Extractionmentioning

confidence: 99%

Face Recognition With Masks Based on Spatial Fine-Grained Frequency Domain Broadening

Chen

Xie

et al. 2022

IEEE Access

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“…Wavelet systems are widely used in applied mathematics for signal analysis and synthesis, compression tasks, data preprocessing and feature extraction for neural networks and so on in a large number of applications (see, for example, [1][2][3][4][5][6]). The symmetry property is among the most desirable features for wavelets.…”

Section: Introductionmentioning

confidence: 99%

Multivariate Symmetric Interpolating Dual Multiwavelet Frames

Krivoshein

2022

Symmetry

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The construction of symmetric multiwavelets in the multivariate case with useful in applications properties is a challenging task, mainly due to the complexity of the matrix extension problem. Nevertheless, for the interpolating case, a general technique can be developed. For an appropriate pair of symmetry group H and matrix dilation M and for a given H-symmetric interpolating refinable matrix mask, a method for the construction of H-symmetric dual refinable matrix masks with a preassigned order of sum rule is suggested. Wavelet matrix masks are constructed using a certain explicit matrix extension algorithm, and their symmetry properties are studied via its polyphase components. The resulting multiwavelet systems form dual multiwavelet frames, where wavelet functions have symmetry properties, preassigned order of vanishing moments and preassigned order of the balancing property. Several examples are presented.

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“…BSS plays an increasingly important role in the field of digital signal processing and has been widely used in communication [27], speech processing [28], fault diagnosis [29,30], seismic exploration [31], biomedicine [32,33], image processing [34], radar [35], and economic data analysis [36]. In blind signal separation, the typical algorithms commonly used include the fast fixed-point algorithm [37], natural gradient algorithm [38], Equivariant Adaptive Separation via Independence (EASI) algorithm [39,40], and Joint Approximation Diagonalization of Eigen-matrices (JADE) algorithm [41,42], etc. Grotas et al [43] developed the constrained maximum likelihood (ML) estimator of the Laplacian matrix for this graph BSS problem with Gaussian-distributed states.…”

Section: Introductionmentioning

confidence: 99%

Multisource Fault Signal Separation of Rotating Machinery Based on Wavelet Packet and Fast Independent Component Analysis

Miao

Zhao

Jia

et al. 2021

International Journal of Rotating Machinery

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The vibration signal of rotating machinery compound faults acquired in actual fields has the characteristics of complex noise sources, the strong background noise, and the nonlinearity, causing the traditional blind source separation algorithm not be suitable for the blind separation of rotating machinery coupling fault. According to these problems, an extraction method of multisource fault signals based on wavelet packet analysis (WPA) and fast independent component analysis (FastICA) was proposed. Firstly, according to the characteristic of the vibration signal of rotating machinery, an effective denoising method of wavelet packet based on average threshold is presented and described to reduce the vibration signal noise. In the method, the thresholds of every node of the best wavelet packet basis are acquired and averaged, and then the average value is used as a global threshold to quantize the decomposition coefficient of every node. Secondly, the mixed signals were separated by using the improved FastICA algorithm. Finally, the results of simulations and real rotating machinery vibration signals analysis show that the method can extract the rotating machinery fault characteristics, verifying the effectiveness of the proposed algorithm.

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Face Recognition Employing DMWT Followed by FastICA

Cited by 14 publications

References 40 publications

Face Recognition With Masks Based on Spatial Fine-Grained Frequency Domain Broadening

Face Recognition With Masks Based on Spatial Fine-Grained Frequency Domain Broadening

Multivariate Symmetric Interpolating Dual Multiwavelet Frames

Multisource Fault Signal Separation of Rotating Machinery Based on Wavelet Packet and Fast Independent Component Analysis

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