A NOVEL MULTI-VIEW FACE DETECTION METHOD BASED ON IMPROVED REAL ADABOOST ALGORITHM

摘要

The present invention relates to a face detection method, and more particularly, to a novel multi-view face detection (MVFD) method based on an improved real adaptive boosting (AdaBoost) algorithm. The improved real AdaBoost algorithm for trained Haar features devises a dynamic weight and a previously divided sample. As compared with an existing real AdaBoost algorithm, a time complexity of a weak classifier is 0(M*N), a time complexity of a strong classifier is 0(T*M*N), and a training speed is reinforced by 0(N). Only twelve category classifiers for the multi-view face detection (MVFD) based on our Haar feature processing are required for generating rotation-out-of-image (ROP) and changing an angle of rotation-in-plane (RIP). The devised algorithm significantly reduces training complexity. In order to use the real AdaBoost algorithm, an LUT weak classifier is used to enhance the Haar features. Instead of each trained pose measuring device, the confidence of the strong classifier defined by the AdaBoost algorithm is used. The first four layers among 16 layers of a cascade classifier are used for pose estimation, and extra calculation for pose estimation is unnecessary. An algorithm test is performed based on CMU and MIT face databases. As a result of the testing, the convergence performance of the improved real AdaBoost algorithm is more enhanced than that of a previous real AdaBoost algorithm. Also, the multi-view face detection system according to the present invention shows a high sensing rate and has an excellent timeliness and a high detection rate.