Abstract:

Applied to face detection, although AdaBoost is one of effective algorithms, it has some limitations. A neighbor-eliminated boosting(NEB) algorithm is proposed to remedy these deficiencies, which is like that the cascaded stage classifiers may unbalance on false reject rate and false accept rate, and that the invalidation of monotonicity assumption may conduce to abortive feature learning. NEB constructs a group of new feature describers linked by two lists, which will lead to correlation of features to simplify training. Experiments demonstrate that NEB algorithm accelerates the training speed and obtain the better performance.

Key words: face detection, AdaBoost, neighbor-eliminated boosting algorithm, a construction linked by two lists, Neyman-Pearson decision rule