Incremental Learning of Patch-Based Bag of Facial Words Representation for Online Face Recognition in Videos

  • Chao Wang
  • Yunhong Wang
  • Zhaoxiang Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7674)


Video-based face recognition is a fundamental topic in image and video analysis, and presents various challenges and opportunities. In this paper, we introduce an incremental learning approach to video-based face recognition, which efficiently exploits the spatiotemporal information in videos. Face image sequences are incrementally clustered based on their descriptors. With the quantization of the facial words, representation of the face image is generated by concatenating the histograms from regions. In the online recognition, a temporal matrix and a voting algorithm are employed to judge a face video’s identity. The proposed method achieves a 100% recognition rate performed on the Honda/UCSD database, and gives near realtime feedback. Experimental results demonstrate the effectiveness and flexibility of our proposed method.


Face Recognition Recognition Rate Face Image Vote Rate Spatial Pyramid Match 
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 2012

Authors and Affiliations

  • Chao Wang
    • 1
  • Yunhong Wang
    • 1
  • Zhaoxiang Zhang
    • 1
  1. 1.School of Computer Science and EngineeringBeihang UniversityChina

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