| 摘要(英) |
In 2020, Coivd-19 has swept the world and let lots of countries be on lockdown. Therefore, people have to stay at home and rely on 3C products to complete their daily purchases. However, since relying heavily on the Internet, the security of the dissemination of important information is facing challenges. Biometric identification has become the latest security protection trend. Biometric recognition is based on biological or behavioral characteristics (such as: face, iris, voiceprint, fingerprint, signature, etc.) to recognize identity. Face recognition is unique, and mobile phones can capture faces, so the cost can be low. Furthermore, the face does not need to be in direct contact when capturing images. This is a great advantage in this precarious time. However, there are still many ways to make the facial recognition system fail, such as print attack or replay attack.
A common face recognition attack — Presentation Attacks (PA) [1], whose main purpose is to impersonate others or avoid being recognized by the system. It mainly includes print attack (such as photos, printed pictures), replay attack (playing video), 3D mask attack; judging the difficulty of a spoof face attack, from easy to difficult: photo attack, video replay attack, print attack, 3D mask attack. Thus, print attack and replay attack are the most common attacks.
We compared the different structures of CNN by using photos and printed pictures as the spoof face dataset, and finally found that although the CNN model is the simplest neural network, it beats most complex models. Our research shows that a CNN model with 5 to 8 layers can achieve 99% accuracy and recall, thus 6 and 7 layers CNN model can get the best performance. |
| 參考文獻 |
[1] Ming, Z., Visani, M., Luqman, M. M., Burie, H.: A SURVEY ON ANTI-SPOOFING METHODS FOR FACE RECOGNITION WITH RGB CAMERAS OF GENERIC CONSUMER DEVICES. arXiv:2010.04145 (2020)
[2] Y. LeCun, P. Haffner, L. Bottou, and Y. Bengio,“Object Recognition with Gradient-Based Learning”, Shape, Contour and Grouping in Computer Vision, pp. 319-345, Oct 1999.
[3] 爾摩,“CONVOLUTIONAL NEURAL NETWORK 介紹”, (2018)。檢自 http://elmer-storage.blogspot.com/2018/07/cnn-convolutional-neural-network-cnn.html ( May. 4, 2021)。
[4] “深度學習：CNN原理”, (2019)。檢自 https://medium.com/@CinnamonAITaiwan/%E6%B7%B1%E5%BA%A6%E5%AD%B8%E7%BF%92-cnn%E5%8E%9F%E7%90%86-keras%E5%AF%A6%E7%8F%BE-432fd9ea4935 ( May. 4, 2021)。
[5] Kingma, D. P., Ba, J. L.: ADAM: A METHOD FOR STOCHASTIC OPTIMIZATION. arXiv:1412.6980 (2019)
[6] 希瓦娜,“論文《Adam:A Method for Stochastic Optimization》心得”, (2019)。檢自 https://zhuanlan.zhihu.com/p/91166049 ( May. 4, 2021)。
[7] “深度學習激勵函數介紹”, (2019)。檢自 https://cvfiasd.pixnet.net/blog/post/275774124-%E6%B7%B1%E5%BA%A6%E5%AD%B8%E7%BF%92%E6%BF%80%E5%8B%B5%E5%87%BD%E6%95%B8%E4%BB%8B%E7%B4%B9( May. 5, 2021)。
[8] “6種激活函數核心知識點，請務必掌握！”, (2018)。檢自 https://kknews.cc/zh-tw/code/ygkq3aa.html ( May. 4, 2021)。
[9] 夏飞,“聊一聊深度学习的activation function”, (2017)。檢自 https://zhuanlan.zhihu.com/p/25110450( May. 4, 2021)。
[10] Clay,“[Machine Learning] ReLU函式介紹與程式實作”, (2019)。
檢自 https://clay-atlas.com/blog/2019/10/20/machine-learning-chinese-relu-function/ ( May. 4, 2021)。
[11] S. Huang,“ReLU 筆記”, (2017)。檢自 https://swhtw.wordpress.com/2017/12/03/relu-%E7%AD%86%E8%A8%98/ ( May. 4, 2021)。
[12] Clay,“[Machine Learning] Sigmoid 函數介紹與程式實作”, (2019)。檢自 https://clay-atlas.com/blog/2019/10/19/machine-learning-chinese-sigmoid-function/ ( May. 4, 2021)。
[13] qq_23304241,“激活函数ReLU、Leaky ReLU、PReLU和RReLU”, (2018)。檢自 https://blog.csdn.net/qq_23304241/article/details/80300149 ( May. 4, 2021)。
[14] He, K., Zhang, X., Ren, S., Sun, J.: Deep Residual Learning for Image Recognition. arXiv:1512.03385(2015)
[15] Howard, A., Sandler, M., Chu, G., Chen, L., Chen, B., Tan, M., Wang, W., Zhu, Y., Pang, R., Vasudevan, V. , Le, Q. V., Adam, H.:SEARCH FOR MOBILENETV3. arXiv:1905.02244 ( 2019)
[16] 無人不智能，機器不學習,“大白话讲解MobileNet-v3”, (2019)。檢自https://blog.csdn.net/qidailiming1994/article/details/103465548 ( May. 4, 2021)。
[17] 黛茜,“Python 3和Keras實現Mobilenet v3”, (2019)。檢自 https://www.jianshu.com/p/9af2ae74ec04 ( May. 4, 2021).
[18] dicksonjyL560101, “從MobileNet看輕量級神經網路的發展”, (2019)。檢自 https://iter01.com/429262.html ( May. 4, 2021)。
[19] qq_29631521, “resnet34
esnet101网络结构图”, (2019)。檢自 https://blog.csdn.net/qq_29631521/article/details/93136216 ( May. 4, 2021).
[20] xiaochus,“xiaochus”, (2020)。檢自 https://github.com/xiaochus/MobileNetV3/tree/master/model ( May. 4, 2021)。
[21] S. Zhang, A. Liu, J. Wan, Y. Liang, G. Guo, S. Escalera, H. J. Escalante, S. Z. Li,“CASIA-SURF: A Large-scale Multi-modal Benchmark for Face Anti-spoofing,”arXiv, Feb. 2020.
[22] D. Timoshenko, K. Simonchik, V. Shutov, P. Zhelezneva, V. Grishkin,“Large Crowdcollected Facial Anti-Spoofing Dataset,”2019 Computer Science and Information Technologies, Sep. 2019.
[23] super13579,“ResNet34_oxflower17”, (2018)。檢自 https://gist.github.com/super13579/9208bb7a1f3c01672ad5058246523706 ( May. 6, 2021)。
[24] A. Rosebrock, “Liveness Detection with OpenCV”, (2019)。檢自 https://www.pyimagesearch.com/2019/03/11/liveness-detection-with-opencv/ ( May. 4, 2021)。 |
[Endnote RIS 格式]
[相關文章]
[文章引用]
[完整記錄]
[館藏目錄]
[檢視]
plurk
funp
live
udn
HD
myshare
netvibes
friend
youpush
delicious
baidu
Google bookmarks
del.icio.us
hemidemi
facebook
twitter
google
reddit