On the performance of wavelet families in face recognition using a multilayer perceptron neural network classifier

Ferhaoui-Cherifi, Chafia; Deriche, Mohamed

doi:10.1109/icetas.2017.8277850

Cited by 1 publication

(1 citation statement)

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“…When we obtain the spatial and frequency-domain characteristic FV, we classify the FV by using multi-layer perceptual classifier [30]. Each test image is assigned its identity information.…”

Section: ) Multilayer Perceptron Feature Classificationmentioning

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: ) Multilayer Perceptron Feature Classificationmentioning

confidence: 99%