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20200626_Trends_in_Machine_Learning.pdf

yamatia
June 26, 2020
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 20200626_Trends_in_Machine_Learning.pdf

yamatia

June 26, 2020
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  1. 顔の研究タスク Q. 研究タスクにはどのようなものが? ・Face Recognition ・Face Detection ・Face Alignment ・Facial

    Expression Recognition ・Face Generation ・3D Face Reconstruction ・Face Swapping ・Talking Face Generation etc….
  2. 既存手法×DeepLearning:DJSTN① ・特徴抽出:Local Binary Pattern & Landmark検出 ・2DCNN+3DCNNを結合したモデルで学習 Dami Jeong, Byung-Gyu

    Kim ,Suh-Yeon Dong.Deep Joint Spatiotemporal Network (DJSTN) for Efficient Facial Expression Recognition.Sensors 2020, 20(7).2020I
  3. 既存手法×DeepLearning:DJSTN② Local Binary Pattern 各画素を周囲画素と比較した相対値 へと変換する手法 Landmark Detection 顔の各パーツのキーポイントを 検

    出する手法。論文ではneutral face との差を計算して入力としている。 Dami Jeong, Byung-Gyu Kim ,Suh-Yeon Dong.Deep Joint Spatiotemporal Network (DJSTN) for Efficient Facial Expression Recognition.Sensors 2020, 20(7).2020I
  4. GANを用いたFER:IFGAN ・GANを用いた表情認識モデル ・cGANで”平均的な”顔画像を生成し、それをもとに分類器学習 Jie Cai ,Zibo Meng, Ahmed Shehab Khan,

    Zhiyuan Li , James O’Reilly, and Yan Tong. Identity-Free Facial Expression Recognition using conditional Generative Adversarial Network .arXiv.2019
  5. Image+赤外線:DBTR-BR 赤外線イメージング+可視 光撮影で表情認識を行うタ スクも存在 (e.g.Oulu-Casia dataset) この論文では、測定IRスペ クトルを復元する手法につ いて主に提案 Huiting

    Wua,Yanshen Liua,Yi Liub,Sanya Liua.Efficient facial expression recognition via convolution neural network and infrared imaging technology Physics & Technology.Volume 102,. 2019
  6. 表情データセットの修正:LDL-ALSG CVPR2020論文 表情データセットの ラベル不確実に対す る、ラベル分布学習 の手法を提案 顔画像からAUとFPを 抽出、kNNグラフ構 築からラベル分布を 学習

    Shikai Chen, Jianfeng Wang, Yuedong Chen, Zhongchao Shi, Xin Geng, Yong Rui,Southeast University 2University of Oxford 3Nanyang Technological University,AI Lab, Lenovo Research.Label Distribution Learning on Auxiliary Label Space Graphs for Facial Expression Recognition.CVPR2020
  7. Reference [1]https://www.pocket-lint.com/phones/news/apple/142207-what-is-apple-face-id-and- how-does-it-work [2] https://filmora.wondershare.com/video-editor/best-face-swap-apps.html [3] https://globalnews.ca/news/6653657/coronavirus-facial-recognition-firm-face-masks/ [4]https://www.researchgate.net/publication/326998708_Facial_Action_Unit_Recognition_Using_Dat a_Mining_Integrated_Deep_Learning [5]

    Shan Li and Weihong Deng. Deep Facial Expression Recognition: A Survey.IEEE.2020 [6] Dami Jeong, Byung-Gyu Kim ,Suh-Yeon Dong.Deep Joint Spatiotemporal Network (DJSTN) for Efficient Facial Expression Recognition.Sensors 2020, 20(7).2020I [7] Huiting Wua,Yanshen Liua,Yi Liub,Sanya Liua.Efficient facial expression recognition via convolution neural network and infrared imaging technology Physics & Technology.Volume 102,. 2019 [8] Shikai Chen, Jianfeng Wang, Yuedong Chen, Zhongchao Shi, Xin Geng, Yong Rui,Southeast University 2University of Oxford 3Nanyang Technological University,AI Lab, Lenovo Research.Label Distribution Learning on Auxiliary Label Space Graphs for Facial Expression Recognition.CVPR2020 [9] Kai Wang,Xiaojiang Peng,Jianfei Yang,Shijian Lu,Yu Qiao. Suppressing Uncertainties for Large-Scale Facial Expression Recognition.CVPR2020 [10]Jie Cai ,Zibo Meng, Ahmed Shehab Khan, Zhiyuan Li , James O’Reilly, and Yan Tong. Identity-Free Facial Expression Recognition using conditional Generative Adversarial Network .arXiv.2019