A real-time face detection method based on blink detection

Aiming at the photo fraud that often occurs in identity verification and the accuracy and robustness of real-time video face recognition, this paper proposes a real-time face detection method based on blink detection. This method first extracts the image texture features through the LBP algorithm, w...

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Bibliographic Details
Published in:IEEE access 2023-01, Vol.11, p.1-1
Main Authors: Qi, Xiaobo, Wu, Chenxu, Qi, Hui, Shi, Ying, Duan, Kaige, Wang, Xiaobin
Format: Article
Language:English
Subjects:
Algorithms
Attention Mechanism
BiLSTM
Ear
Face recognition
Faces
Feature extraction
Feature maps
Fraud
Illumination
Lighting
Real time
Real-time systems
Residual neural networks
Spoofing
SPP
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Summary:Aiming at the photo fraud that often occurs in identity verification and the accuracy and robustness of real-time video face recognition, this paper proposes a real-time face detection method based on blink detection. This method first extracts the image texture features through the LBP algorithm, which eliminates the problem of illumination changes to a certain extent. Then the extracted features are input into the ResNet network, and the facial feature extraction is enhanced by adding an attention mechanism is added to enhance the face feature extraction. Meanwhile, the BiLSTM method is used to extract the temporal characteristics of images from different angles or at different times to obtain more facial details. In addition, the fusion of local and global features is realized by SPP pooling, which enriches the expression ability of feature maps and improves detection accuracy. Finally, the eye EAR value is calculated by the face key point detection technology to achieve face anti-spoofing, and then the real-time face recognition against fraud is realized. The experimental results show that the algorithm proposed in this paper has good accuracy on NUAA, CASIA-SURF and CASIA-FASD datasets, which can reach 99.48%, 98.65% and 99.17%, respectively.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2023.3257986