Face Detection Using Quantum-inspired Evolutionary Algorithm

Video target tracking is an important part of computer vision research field, Face automated detection technology is the hot topic of pattern recognition and image processing. With the development of lives, people are eager to get the fast identity recognition technology. As the one of the most important biology recognition technology, the face detection becomes more and more valued. The PCA algorithm due to its high efficiency has been widely used in the field of pattern recognition, it has become one of the important methods of face detection. Based on the analysis of the characteristics of PCA algorithm we propose the improvement, which improves the detection success in the process of face detection, and effectively reduce the false alarm rate.This thesis proposes a new approach based on QEA according to the characteristics of PCA. Because of the threshold of PCA is selected with blindness. That lead to a unsatisfactory detection result. In this thesiswe use QEA to search weight factor in the PCA process, as soon as we determed the weight factor the threshold was selected, so that the selection of threshold is justified, so we can select the best threshold efficient. In addition, this paper constructs the AR and RIT face dataset, both of the improved detection method and PCA method is applied to the dataset, and we analyse and compare the experimental results. Finally the experimental proved that QEA improved algorithm is better than PCA method in the success rate and false alarm rate.