Research On Real Time Face Detection

As a hot issue in the research field of computer vision, face detection has been studied for decades. In the early years, face detection, though was proposed as the first step of face recognition, was viewed as far easier than the latter. Nevertheless, as the study goes deeper, face detection now is considered to be as difficult as, if not more difficult than, face recognition.As the pre-process of face recognition, face detection has its unalterable value. On the other hand, for its application in the field of biometric surveillance and automatic tracking, the real time requirement of face detection is becoming higher. Although the proposal of the combination of Boosting algorithm and cascade classifier by Paul Viola drew a great advance of real time face detection, there are still space for the improvement of classifier training and detection.Therefore an improved training algorithm of AdaBoost is proposed to solve the problem of long period of training in this thesis. According to this improved algorithm, threshold selection of weak classifier is processed in a single pass to reduce time complexity. And computational results of sorted feature values are buffered, cutting down the number of re-calculations significantly.Also a novel cascaded classifier constructing method is proposed...