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Related Experiment Videos

Dynamic texture recognition using local binary patterns with an application to facial expressions.

Guoying Zhao1, Matti Pietikäinen

  • 1Machine Vision Group, Department of Electrical and Information Engineering, University of Oulu, FI-90014 Oulu, Finland. gyzhao@ee.oulu.fi

IEEE Transactions on Pattern Analysis and Machine Intelligence
|April 14, 2007
PubMed
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This study introduces a novel method for dynamic texture (DT) recognition using Volume Local Binary Patterns (VLBP) and Local Binary Patterns on Three Orthogonal Planes (LBP-TOP). The approach achieves superior performance in texture and facial expression analysis.

Area of Science:

  • Computer Vision
  • Image Processing
  • Pattern Recognition

Background:

  • Dynamic textures (DTs) extend static texture analysis to the temporal domain, requiring advanced description and recognition methods.
  • Existing texture analysis methods often struggle with the temporal dynamics inherent in DTs.
  • Facial expression analysis presents a specific challenge within DT recognition due to localized spatial and temporal information.

Purpose of the Study:

  • To propose a novel and computationally efficient approach for dynamic texture recognition.
  • To adapt and extend the proposed method for facial image analysis, specifically for recognizing facial expressions.
  • To demonstrate the effectiveness and advantages of the new method compared to existing techniques.

Main Methods:

Related Experiment Videos

  • Modeling dynamic textures using Volume Local Binary Patterns (VLBP), which integrates motion and appearance.
  • Employing Local Binary Patterns on Three Orthogonal Planes (LBP-TOP) for computational simplicity and extensibility.
  • Developing a block-based method to capture local information and spatial locations crucial for dynamic events like facial expressions.

    • Main Results:

    • VLBP and LBP-TOP methods significantly outperformed previous approaches on the DynTex and MIT dynamic texture databases.
    • The block-based method achieved excellent results in recognizing facial expressions on the Cohn-Kanade database.
    • The proposed approach demonstrated robustness to monotonic gray-scale changes and computational simplicity.

    Conclusions:

    • The proposed VLBP and LBP-TOP methods offer a powerful and efficient solution for dynamic texture recognition.
    • The block-based extension is highly effective for analyzing dynamic events in facial images.
    • The approach's advantages include local processing, robustness, and computational efficiency, making it suitable for various applications.