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个人简介
His research interests are in the fields of Computer Vision and Machine Learning. In particular, applications to human health, augmented reality, virtual reality, and methods that focus on the data (not the model). He is directing the Human Sensing Laboratory.
Dr. De la Torre’s research interests include machine learning, signal processing and computer vision, with a focus on understanding human behavior from multimodal sensors (e.g. video, body sensors). I am particularly interested in three main topics:
Component Analysis (CA): CA methods (e.g. kernel PCA, Normalized Cuts, Multidimensional Scaling) are a set of algebraic techniques that decompose a signal into relevant components for classification, clustering, modeling, or visualization. I am interested in using CA methods to efficiently and robustly learn models from large amounts of high dimensional data. The theoretical focus of my work is to develop a unification theory for many component analysis methods. I lead the Component Analysis Lab at CMU
Human Sensing: Modeling and understanding human behavior from sensory data (e.g. video, motion capture, audio). This work is motivated by applications in the fields of human health, computer graphics, machine vision, biometrics, and human-machine interfacing. I co-lead the Human Sensing Lab at CMU
Face Analysis: Developing algorithms for real-time face tracking, recognition, and expression/emotion analysis.
Dr. De la Torre’s research interests include machine learning, signal processing and computer vision, with a focus on understanding human behavior from multimodal sensors (e.g. video, body sensors). I am particularly interested in three main topics:
Component Analysis (CA): CA methods (e.g. kernel PCA, Normalized Cuts, Multidimensional Scaling) are a set of algebraic techniques that decompose a signal into relevant components for classification, clustering, modeling, or visualization. I am interested in using CA methods to efficiently and robustly learn models from large amounts of high dimensional data. The theoretical focus of my work is to develop a unification theory for many component analysis methods. I lead the Component Analysis Lab at CMU
Human Sensing: Modeling and understanding human behavior from sensory data (e.g. video, motion capture, audio). This work is motivated by applications in the fields of human health, computer graphics, machine vision, biometrics, and human-machine interfacing. I co-lead the Human Sensing Lab at CMU
Face Analysis: Developing algorithms for real-time face tracking, recognition, and expression/emotion analysis.
研究兴趣
论文共 327 篇作者统计合作学者相似作者
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arXiv (Cornell University) (2023)
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CoRR (2023): 3946-3957
Jianjin Xu,Saman Motamed, Praneetha Vaddamanu,Chen Henry Wu, Christian Haene, Jean-Charles Bazin,Fernando de la Torre
CoRR (2023)
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Aashish Rai, Hiresh Gupta, Ayush Pandey,Francisco Vicente Carrasco, Shingo Jason Takagi, Amaury Aubel,Daeil Kim,Aayush Prakash,Fernando de la Torre
CoRR (2023)
2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)pp.826-836, (2023)
CoRR (2023): 22721-22730
Shubhra Aich, Jesús Ruiz-Santaquiteria, Zhenyu Lu, Prachi Garg,K. J. Joseph, Alvaro Fernandez Garcia,Vineeth N. Balasubramanian, Kenrick Kin,Chengde Wan,Necati Cihan Camgöz,Shugao Ma,Fernando De la Torre
Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)pp.20901-20910, (2023)
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