A Robust Face Recognition Approach against Variant Illumination
In order to alleviate the effect of the light illumination and environment noise, a robust face recognition method is proposed in this paper based on Curvelet transform and local ternary pattern. The Curvelet Transform (CT) is a new anisotropic multi-resolution technique, which can effectively retain image edge information. Local Ternary Pattern (LTP) is an extended version of Local Binary Pattern (LBP). First the face images are decomposed into three parts by CT, and then we process the coefficients of its first band by using logarithm computation and LTP, while directly delete the redundant highest frequency information in the third part with an aim of removing the environment noise and the noisy information at the intersection of the light and the object. Then we select the principal features from the second part coefficients by using Principal Component Analysis (PCA). Finally, the face recognition is done by using Linear Discriminant Analysis (LDA) with the preprocessed first part features and the second part features obtained from PCA. Extensive experiments show that the proposed method can alleviate the effect of the illumination and environment noise effectively, which achieves better face recognition rate than the Curvelet+PCA+LDA.
Face recognition Illumination CT LTP PCA LDA
ZHOU Lijian LIU Wanquan WANG Ying
School of Communication and Electronic Engineering, Qingdao Technological University, Qingdao 266033 Dept. of Computing, Curtin University, Perth WA, 6102, Australia SINOPEC Petroleum Exploration and Production Research Institute, Beijing 100083, China
国际会议
The 31st Chinese Control Conference(第三十一届中国控制会议)
合肥
英文
3891-3896
2012-07-01（万方平台首次上网日期，不代表论文的发表时间）