Facial descriptor for Kinect depth using inner–inter-normal components local binary patterns and tensor histograms

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• 作者：Guangling Sun ; Yong Dong ; Xiaofei Zhou ; Zhi Liu
• 关键词：Facial analysis ; Kinect depth ; Facial descriptor ; Tensor
• 刊名：Machine Vision and Applications
• 出版年：2016
• 出版时间：October 2016
• 年：2016
• 卷：27
• 期：7
• 页码：997-1003
• 全文大小：1,767 KB
• 刊物类别：Computer Science
• 刊物主题：Pattern Recognition
  Image Processing and Computer Vision
  Communications Engineering and Networks
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1432-1769
• 卷排序：27

文摘

RGB-D data collected from Microsoft Kinect are an easily available media providing the additional depth information besides RGB, which has great potentials in improving the performance of the facial analysis applications. In this paper, we focus on facial descriptor extraction from depth channel. First, we design a facial shape descriptor based on local binary patterns encoding from surface normal. The novelty lies in that it considers local structures involved in both inner component and inter-components of surface normal. Second, we propose a tensor representation for histogram arrays embedded in multidimensional space and further use multilinear principal component analysis to obtain an optimal trade-off between efficacy and efficiency. The experiments conducted on two publicly available databases, named CurtinFaces and Eurecom, have demonstrated the promising results achieved by proposed schemes on identity and ethnicity classification.