Spatio-temporal Embedding for Statistical Face Recognition from Video

  • Wei Liu
  • Zhifeng Li
  • Xiaoou Tang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3952)


This paper addresses the problem of how to learn an appropriate feature representation from video to benefit video-based face recognition. By simultaneously exploiting the spatial and temporal information, the problem is posed as learning Spatio-Temporal Embedding (STE) from raw video. STE of a video sequence is defined as its condensed version capturing the essence of space-time characteristics of the video. Relying on the co-occurrence statistics and supervised signatures provided by training videos, STE preserves the intrinsic temporal structures hidden in video volume, meanwhile encodes the discriminative cues into the spatial domain. To conduct STE, we propose two novel techniques, Bayesian keyframe learning and nonparametric discriminant embedding (NDE), for temporal and spatial learning, respectively. In terms of learned STEs, we derive a statistical formulation to the recognition problem with a probabilistic fusion model. On a large face video database containing more than 200 training and testing sequences, our approach consistently outperforms state-of-the-art methods, achieving a perfect recognition accuracy.


Face Recognition Video Sequence Audio Signal Frame Synchronization Dimensionality Reduction Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Wei Liu
    • 1
  • Zhifeng Li
    • 1
  • Xiaoou Tang
    • 1
    • 2
  1. 1.Department of Information EngineeringThe Chinese University of Hong KongHong KongChina
  2. 2.Microsoft Research AsiaBeijingChina

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