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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2022.11
- 수록면
- 1,582 - 1,590 (9page)
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초록· 키워드
오류제보하기
Due to the situation of the widespread of the coronavirus, which causes the problem of lack of face image data occluded by masks at recent time, in order to solve the related problems, this paper proposes a method to generate face images with masks using a combination of generative adversarial networks and spatial transformation networks based on CNN model. The system we proposed in this paper is based on the GAN, combined with multi-scale convolution kernels to extract features at different details of the human face images, and used Wasserstein divergence as the measure of the distance between real samples and synthetic samples in order to optimize Generator performance. Experiments show that the proposed method can effectively put masks on face images with high efficiency and fast reaction time and the synthesized human face images are pretty natural and real.
목차
ABSTRACT
1. INTRODUCTION
2. RELATED TECHNOLOGIES
3. The combination of STN and WGAN- div generative adversarial network using Pyramid convolutional nerual network
4. EXPERIMENT RESULT
5. CONCLUSION
REFERENCE
참고문헌 (14)
참고문헌 신청
J. Wang and E.J. Lee, “Low Resolution Rate Face Recognition Based on Multi-scale CNN,” Journal of Journal of Korea Multimedia Society, Vol. 21, No. 12, pp. 1467-1472, 2018.
T. Yaniv, M. Yang, R.M. Aurelio, and L. Wolf, “Deepface: Closing the Gap to Human Level Performance in Face Verification,” Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1701-1708, 2014.
W.K. Xu and E.J. Lee, “Human-computer Catural User Interface Based on Hand Motion Detection and Tracking,” The Journal of Multimedia Information System, Vol. 15, No. 4, pp. 501-507, 2012.
S. Florian, K. Dmitry, and J. Philbin, “Facenet: A Unified Embedding for Face Recognition and Clustering,” Proceedings of 2015 IEEE Conference on Computer Vision and Patten Recognition, pp. 815-823, 2015.
A.R. Syafeeza, M. Khalil-Hani, S.S. Liew, and R. Bakhteri, “Convolutional Neural Network for Face Recognition with Pose and Illumination Variation,” Journal of IJET 2014, Vol. 6, No. 1, pp. 44-57, 2014.
T. Yaniv, M. Yang, R.M. Aurelio, and L. Wolf, “Deepface: Closing the Gap to Human Level Performance in Face Verification,” Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1701-1708, 2014.
W.K. Xu and E.J. Lee, “Human-computer Catural User Interface Based on Hand Motion Detection and Tracking,” The Journal of Multimedia Information System, Vol. 15, No. 4, pp. 501-507, 2012.
S. Florian, K. Dmitry, and J. Philbin, “Facenet: A Unified Embedding for Face Recognition and Clustering,” Proceedings of 2015 IEEE Conference on Computer Vision and Patten Recognition, pp. 815-823, 2015.
A.R. Syafeeza, M. Khalil-Hani, S.S. Liew, and R. Bakhteri, “Convolutional Neural Network for Face Recognition with Pose and Illumination Variation,” Journal of IJET 2014, Vol. 6, No. 1, pp. 44-57, 2014.
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