期刊文献+

基于位平面和协作表示的人脸识别算法 被引量:5

Face Recognition Using Bit-Plane Images and Collaborative Representation
下载PDF
导出
摘要 为提高人脸识别的准确性和高效性,提出了一种基于位平面信息的协作表示人脸识别算法,利用8位灰度图像的8个位平面和协作表示原理进行人脸识别.采用直方图均衡算法增强图像的对比度,并将均衡后的图像分解成8个包含有不同信息的二进制位平面图像.根据各位平面图像包含的识别信息量对它们进行加权求和,构造虚拟人脸图像.最后在虚拟人脸图像数据库上采用协作表示进行识别.实验结果表明,该算法具有较高的识别率和较快的识别速度. As a new representation approach, collaborative representation was employed to collaboratively represent the query image by all training images from all classes. In this paper, a novel face recognition algorithm using collaborative representation based on the binary bit-plane images had been proposed. The original gray images are firstly equalized and decomposed into 8 bit-plane images. And then the new virtual face images, which contained more discrimination information, were constructed by the 8 bit-plane images and the weight vector that was calculated by the recognition rate and the order of the bit-plane. Finally the collaborative representation was performed on the virtual face images. Experimental results show the proposed combination approach has very competitive classification rate and recognition speed.
出处 《北京理工大学学报》 EI CAS CSCD 北大核心 2014年第9期966-971,共6页 Transactions of Beijing Institute of Technology
基金 山东省科技攻关计划资助项目(2010GF10243)
关键词 直方图均衡 位平面 协作表示 加权和图像 histogram equalization bit-plane collaborative representation weight sum image
  • 相关文献

参考文献11

  • 1Turk M, Pentland A. Eigenfaces for recognition[J]. Cognitive Neuroscience, 1991,3(1):71-86.
  • 2Belhumeur P N, Hespanha J P, Kriegman D J, et al. Fisherfaces: recognition using class specific linear projection[J]. IEEE Trans on Pattern Analysis and Machine Intelligence, 1997,19(7):711-720.
  • 3He X, Yan S, Hu Y, et al. Learning a locality preserving subspace for visual recognition[C]//Proceedings of IEEE International Conference on Computer Vision. Nice, France: [s.n.], 2003:385-392.
  • 4Wang H, Leng Y, Wang Z, et al. Application of image correction and bit-plane fusion in generalized PCA based face recognition[J]. Pattern Recognition Letters, 2007,28(16):2352-2358.
  • 5Yang A, Wright J, Ma Y, et al. Feature selection in face recognition: a sparse representation perspective, technical[S]. Berkeley, USA: [s.n.], 2007.
  • 6Wright J, Yang A, Ganesh A, et al. Robust face recognition via sparse representation[J]. IEEE Trans on Pattern Analysis and Machine Intelligence, 2009,31(2):210-227.
  • 7Huang J Z, Huang X L, Metaxas D. Simultaneous image transformation and sparse representation recovery[C]//Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition. Anchorage, USA: [s.n.], 2008:1-8.
  • 8Yang M, Zhang L. Gabor feature based sparse representation for face recognition with gabor occlusion dictionary[C]//Proceedings of the 11th European conference on Computer vision: Part VI. Heraklion,Crete, Greece:[s.n.], 2010:448-462.
  • 9Yang M, Zhang L, Yang J, et al. Robust sparse coding for face recognition[C]//Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition. Providence, USA:[s.n.], 2010:625-632.
  • 10Zhang L, Yang M, Feng X. Sparse representation or collaborative representation: which helps face recognition[C]//Proceedings of IEEE International Conference on Computer Vision. Barcelona, Spain:[s.n.], 2011:471-478.

同被引文献24

引证文献5

二级引证文献25

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部