Face Recognition Based on Gabor with 2DPCA and PCA

摘要：

In this paper, a new method combined Two-Dimensional Principal Component Analysis (2DPCA) with Principal Component Analysis (PCA) is proposed to extract Gabor features for face recognition. Gabor wavelet has been widely used in the face recognition task because it’s good imitation of human visual. However, the huge redundancy of Gabor features limits its application. When processing an image use a group Gabor nuclear with five scale and eight directions, the date obtained is enormous. The traditional Two-Dimensional Principal Component Analysis (2DPCA) can limit relativity between Columns, but the number of features is still large, which affects the speed of classification. To resolve this problem, the author uses a method based on Gabor wavelet matrix applied to 2DPCA features matrix and Principal Component Analysis (PCA) for the feature extraction in this paper. The experiment results showed that the performance is superior to single 2DPCA or Gabor with 2DPCA.

关键词： Two-dimensional Gabor wavelets Principal Component Analysis(PCA) Two-Dimensional Principal Component Analysis (2DPCA) face recognition

作者: Zhao Lihong Yang Caikun Pan Feng Wang Jiahe

作者单位: College of Information Science and Engineering Northeastern University, Shenyang, 110819, China

会议类型: 国际会议

会议名称: The 24th Chinese Control and Decision Conference (第24届中国控制与决策学术年会 2012 CCDC)

会议地点: 太原

会议语种:英文

页码: 2644-2647

在线出版日期: 2012-05-23（万方平台首次上网日期，不代表论文的发表时间）