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研究生:阮功信
研究生(外文):Tin Nguyen Cong
論文名稱:人臉反欺騙運用預訓練及多分支 CNN
論文名稱(外文):Face Anti Spoofing Using Autoencoder Pretraining In Multi-Branch CNN
指導教授:王家慶教授
指導教授(外文):Jia-Ching Wang
學位類別:碩士
校院名稱:國立中央大學
系所名稱:資訊工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2019
畢業學年度:107
語文別:英文
論文頁數:57
中文關鍵詞:深度學習色彩空間
外文關鍵詞:deep learningcolor space
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本文提出了一種基於深度學習算法的人臉分類系統。該系統能夠將真實和假面
部與普通相機拍攝的 RGB 圖像區分開.
為此,我們構建了一個由 4 部分組成的系統:RGB 圖像處理,HSV 圖像處理,
YCrCb 圖像處理和分類。 對於圖像處理的前 3 個部分,模型將具有要考慮的對
象的不同視點,使得分類可以使得最準確的結論成為可能。 此外,為了實現最
佳處理性能,我們還包括編碼器和解碼器結構模型,它們消除了不必要的組件 ,
並幫助模型僅關注它所提供的組件。 很重要,最重要的是,這種結構有助於降
低模型的複雜性。
在實驗過程中,我們發現數據處理中出現了一些問題,即研究數據與實際數據
不符。 為了創建一個在實際研究和運營數據上取得良好結果的模型,我們在進
行培訓之前對數據進行了一些特殊的調整。 實驗結果表明,我們的系統在公共
數據庫上給出了非常高的結果。
In this thesis, we propose a face classification system based on deep learning algorithm. This system is capable of distinguishing real and fake faces from RGB images taken by a normal camera.
To do that, we have built a system of 4 parts: RGB image processing, HSV image processing, YCrCb image processing, and classification. With the first 3 parts of image processing, the model will have different viewpoints of the object to be considered so that the classification can make the most accurate conclusion possible. In addition, in order to achieve optimal processing performance, we include encoder and decoder structure models, which eliminate unnecessary components and help the model focus only on the components it gives. is important, and most importantly, this structure helps reduce the complexity of the model.
In the process of experimentation, we found some problems arising in the processing of data, namely that the research data does not match the actual data. In order to create a model for good results on actual research and operational data, we have applied a number of special tweaks to the data before being put into training. Experimental results indicate that our system gives a very high result on public databases.
1 Introduction
1.1 Motivation . . . . . . . . . . . . . . . . . . . . 1
1.2 Problems description . . . . . . . . . . . . . . . . 2
1.3 Thesis overview. . . . . . . . . . . . . . . . . . . 3
2 Related work
2.1 Single Shot Detector (SSD) based on ResNet . .. . . 4
2.2 ResNet . . . . . . . . . . . . . . . . . . . . . . . 8
2.3 Image transformations . . . . . . . . . . . . . . 15
2.4 Autoencoder . . . . . . . . . . . . . . . . . . . . 25
3 Proposed method
3.1 Observation . . . . . . . . . . . . . . . . . . . . 27
3.2 System overview . . . . . . . . . . . . . . . . . . 28
4 Experiment
4.1 Data overview . . . . . . . . . . . . . . . . . . . 32
4.2 Preprocessing . . . . . . . . . . . . . . . . . . . 34
4.3 Evaluation metrics . . . . . . . . . . . . . . . . 36
4.4 Results . . . . . . . . . . . . . . . . . . . . . . 38
5 Conclusion. . . . . . . . . . . . . . . . . . . . . . 42
6 Bibliographies. . . . . . . . . . . . . . . . . . . . 43
1. Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., LeCun, Y.: Overfeat:Integratedrecognition, localization and detection using convolutional networks. In:ICLR. (2014)

2. Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified,real-timeobject detection. In: CVPR. (2016)

3. Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards real-time object detectionwith region proposal networks. In: NIPS. (2015)

4. Erhan, D., Szegedy, C., Toshev, A., Anguelov, D.: Scalable object detection using deepneural networks. In: CVPR. (2014)

5. Erhan, D., Szegedy, C., Toshev, A., Anguelov, D.: Scalable object detection using deepneural networks. In: CVPR. (2014)

6. Szegedy, C., Reed, S., Erhan, D., Anguelov, D.: Scalable, high-quality object detection.arXiv preprint arXiv:1412.1441 v3 (2015)

7. He, K., Zhang, X., Ren, S., Sun, J.: Spatial pyramid pooling in deep convolutional networksfor visual recognition. In: ECCV. (2014)

8. Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation.In: CVPR. (2015)

9. Hariharan, B., Arbelaez, P., Girshick, R., Malik, J.: Hypercolumns for object segmentationand fine-grained localization. In: CVPR. (2015)

10. Liu, W., Rabinovich, A., Berg, A.C.: ParseNet: Looking wider to see better. In:ILCR. (2016)

11. Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: Object detectors emerge in deepscene cnns. In: ICLR. (2015)

12. L. Li, X. Feng, Z. Boulkenafet, Z. Xia, M. Li, and A. Hadid. An original face anti-spoofing approach using partial convolutional neural network. In IPTA, 2016. 1, 2, 7

13. J. Yang, Z. Lei, and S. Z. Li. Learn convolutional neural network for face anti-spoofing. arXiv:1408.5601, 2014. 2, 7

14. Z. Boulkenafet, J. Komulainen, and A. Hadid. Face anti-spoofing based on color texture analysis. In ICIP, 2015. 7

15. Z. Boulkenafet, J. Komulainen, and A. Hadid. Face anti-spoofing using speeded-up robust features and fisher vector encoding. IEEE Signal Processing Letters, 2017. 7


16. K. Patel, H. Han, A. K. Jain, and G. Ott. Live face video vs. spoof face video: Use of moiré patterns to detect replay video attacks. In ICB, 2015. 7

17. Y. Atoum, Y. Liu, A. Jourabloo, and X. Liu. Face anti-spoofing using patch and depth-based cnns. In IJCB, 2017. 1, 2, 7

18. O. Lucena, A. Junior, V. Moia, R. Souza, E. Valle, and R. Lotufo. Transfer learning using convolutional neural net works for face anti-spoofing. In ICIAR, 2017. 7

19. Jianzhu Guo, Xiangyu Zhu, Jinchuan Xiao, Zhen Lei, Genxun Wan, Stan Z. Li. Improving Face Anti-Spoofing by 3D Virtual Synthesis. 2019
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