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Weighted Block-Sparse Low Rank Representation for Face Clustering in Videos             


In this paper, we study the problem of face clustering in videos. Specifically, given automatically extracted faces from videos and two kinds of prior knowledge (the face track that each face belongs to, and the pairs of faces that appear in the same frame), the task is to partition the faces into a given number of disjoint groups, such that each group is associated with one subject. To deal with this problem, we propose a new method called weighted block-sparse low rank representation (WBSLRR) which considers the available prior knowledge while learning a low rank data representation, and also develop a simple but effective approach to obtain the clustering result of faces. Moreover, after using several acceleration techniques, our proposed method is suitable for solving large-scale problems. The experimental results on two benchmark datasets demonstrate the effectiveness of our approach.

Video Spotlight
1-minute brief introduction on Youtube

An illustration
An illustration   

WBSLRR problem
WBSLRR problem


  author    = {Shijie Xiao and
               Mingkui Tan and
               Dong Xu},
  title     = {Weighted Block-Sparse Low Rank Representation for Face Clustering in Videos},
  booktitle = {European Conference on Computer Vision},  
  year      = {2014},
  pages     = {123--138},
BF0502:      online available on the project page of the VGG group
Notting-Hill: can be obtained from Dr. Baoyuan Wu (i.e., the first author of [28]).  We thank Dr. Baoyuan Wu for sharing this dataset~

To download the Matlab codes, click the "" on the bottom right.  ^_^

Shijie Xiao,
Oct 31, 2014, 11:05 AM