Robust dual-stage face recognition method using PCA and high-dimensional-LBP
Kai Liu, Seungbin B. Moon · 2016
In this paper, we propose a dual-stage face recognition method which utilized both holistic and local features-based recognition algorithms. In the first stage Principal Components Analysis (PCA) is utilized to recognize test image. If the confidence level test is passed, the recognition process will be terminated. Otherwise, the second stage where High Dimensional Local Binary Patterns (HDLBP) is employed will be pursued. The performance of this hybrid method is evaluated on CMU-PIE database, and we obtain improved recognition rate than PCA alone.