A Real-Time Face Detection Method Based on Blink Detection
Face anti-spoofing refers to the computer determining whether the face detected is a real face or a forged face. In user authentication scenarios, photo fraud attacks are easy to occur, where an illegal user logs into the system using a legitimate user’s picture. Aiming at this problem an...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10073525/ |
| _version_ | 1797855337561194496 |
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| author | Hui Qi Chenxu Wu Ying Shi Xiaobo Qi Kaige Duan Xiaobin Wang |
| author_facet | Hui Qi Chenxu Wu Ying Shi Xiaobo Qi Kaige Duan Xiaobin Wang |
| author_sort | Hui Qi |
| collection | DOAJ |
| description | Face anti-spoofing refers to the computer determining whether the face detected is a real face or a forged face. In user authentication scenarios, photo fraud attacks are easy to occur, where an illegal user logs into the system using a legitimate user’s picture. Aiming at this problem and the influence of illumination in real-time video face recognition, this paper proposes a real-time face detection method based on blink detection. The 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. |
| first_indexed | 2024-04-09T20:22:08Z |
| format | Article |
| id | doaj.art-7370de0fb2cf47e38928aaa195a40692 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-09T20:22:08Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-7370de0fb2cf47e38928aaa195a406922023-03-30T23:02:01ZengIEEEIEEE Access2169-35362023-01-0111281802818910.1109/ACCESS.2023.325798610073525A Real-Time Face Detection Method Based on Blink DetectionHui Qi0https://orcid.org/0000-0002-9930-9088Chenxu Wu1Ying Shi2Xiaobo Qi3Kaige Duan4Xiaobin Wang5School of Computer Science and Technology, Taiyuan Normal University, Jinzhong, ChinaSchool of Computer Science and Technology, Taiyuan Normal University, Jinzhong, ChinaSchool of Computer Science and Technology, Taiyuan Normal University, Jinzhong, ChinaSchool of Computer Science and Technology, Taiyuan Normal University, Jinzhong, ChinaSchool of Computer Science and Technology, Taiyuan Normal University, Jinzhong, ChinaSchool of Computer Science and Technology, Taiyuan Normal University, Jinzhong, ChinaFace anti-spoofing refers to the computer determining whether the face detected is a real face or a forged face. In user authentication scenarios, photo fraud attacks are easy to occur, where an illegal user logs into the system using a legitimate user’s picture. Aiming at this problem and the influence of illumination in real-time video face recognition, this paper proposes a real-time face detection method based on blink detection. The 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.https://ieeexplore.ieee.org/document/10073525/Attention mechanismBiLSTMface recognitionSPP |
| spellingShingle | Hui Qi Chenxu Wu Ying Shi Xiaobo Qi Kaige Duan Xiaobin Wang A Real-Time Face Detection Method Based on Blink Detection IEEE Access Attention mechanism BiLSTM face recognition SPP |
| title | A Real-Time Face Detection Method Based on Blink Detection |
| title_full | A Real-Time Face Detection Method Based on Blink Detection |
| title_fullStr | A Real-Time Face Detection Method Based on Blink Detection |
| title_full_unstemmed | A Real-Time Face Detection Method Based on Blink Detection |
| title_short | A Real-Time Face Detection Method Based on Blink Detection |
| title_sort | real time face detection method based on blink detection |
| topic | Attention mechanism BiLSTM face recognition SPP |
| url | https://ieeexplore.ieee.org/document/10073525/ |
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