In practical systems, face recognition under unconstrained conditions is a very challenging task, where their input images are first pre-processed and initially aligned by a face detection algorithm. However, there are still some residual localisation errors after the initial alignment. If we do not take these errors into account, the recognition performance should be greatly degraded for most face recognition algorithms. Generally, when designing a practical face recognition system, we need to compromise the capability of residual error tolerance and the discriminating capability. Although it is feasible to apply an iterative alignment algorithm to fine-tune alignment, it will increase the computation load significantly. In this study, we propose an adaptive twostage face recognition system consisting of two block-based recognition stages with a relatively larger cell size (i.e. the size of local regions) in the first stage to provide sufficient tolerance for geometric errors followed by a smaller one in the second stage to accurately evaluate a most probable candidate subset, which is adaptively determined according to the proposed confidence measure. In addition, an iterative gradient-based alignment algorithm is incorporated into the two-stage system to refine the alignment such that the recognition performance can be improved and the computation load can be saved simultaneously.
All Science Journal Classification (ASJC) codes
- Computer Vision and Pattern Recognition