Fernando De la Torre

本文探讨了高维数据的统计建模方法及其在计算机视觉领域的应用,特别是面部跟踪、建模及识别等方面的技术进展。重点介绍了多种组件分析技术,如鲁棒主成分分析(RPCA)、参数化主成分分析(PPCA)等,并展示了如何利用这些方法进行面部外观模型的自动学习及视频中的面部识别。

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http://www.salle.url.edu/~ftorre/

RESEARCH INTERESTS

  • Statistical modeling of high dimensional data with Component Analysis.
  • Optimization methods for signal processing and computer vision.
  • Robust methods in computer vision.
  • Face modeling and tracking.
  • Face Recognition.
  • Mining activity in multimodal data.
  • Multimodal meeting understanding.
  •  COMPONENT ANALYSIS & SUBSPACE METHODS

    • Robust Principal Component Analysis (RPCA)
    • Parameterized Principal Component Analysis (PPCA)
    • Multimodal Oriented Discriminant Analysis (MODA)
    • Representational Oriented Component Analysis ( ROCA )
    • Dynamic Coupled Component Analysis (DCCA)
    • Coordinating Component Analysis (CCA)

    FACE TRACKING - MODELING

    • Automatic learning of facial appearance models. (Video 1)
    • Tracking with appearance models. (Video1) (Video2)
    • Speech driven with appearance models. (Video)

    FACE RECOGNITION

  • Face Recognition from video. (Video)
  • Face Recognition with 1 training sample per class
  •  

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