A two-stage low complexity face recognition system for face images with alignment errors

Face recognition for images acquired from uncontrollable environment and target positions is a challenging task. These input images are first pre-processed and initially aligned by the face detection algorithm. However, there are still some residual geometric errors after the initial alignment by the face detection algorithm. If we don´t take these errors into account, the recognition performance should be unacceptable. Although some iterative optimization algorithms can be used to fine-tune alignment during recognition, it increases computation load significantly. A two-stage face recognition system is proposed which comprises a block-based recognition algorithm to provide sufficient tolerance for geometric errors and then followed by a pixel-based recognition algorithm which only needs to evaluate a candidate subset from the previous stage. From simulation results, we find that this proposed system can reduce the average computation complexity about 69% and achieve promising performance.