CAJ | 학술논문

文中提出一种基于Haar-Like T特征的人脸检测算法。 Haar-Like T特征是在Haar-Like特征的基础上的扩展，由于人脸五官分布的特殊性，在人脸模型上可以找到大量T字型结构特征。结合Haar-Like 矩形特征描述人脸纹理的原理，文中提出4种类似Haar-Like特征的Haar-Like T特征，并将这些Haar-Like T特征与现有的Haar-Like特征一起输入Adaboost分类器进行特征选择，最终构建出分类性能强大的级联分类器并用于人脸检测。人脸检测实验表明该算法的有效性和优越性，其与Haar-Like分类器、LBP分类器等传统的人脸检测分类器相比获得更好的效果。
문중제출일충기우Haar-Like T특정적인검검측산법。 Haar-Like T특정시재Haar-Like특정적기출상적확전，유우인검오관분포적특수성，재인검모형상가이조도대량T자형결구특정。결합Haar-Like 구형특정묘술인검문리적원리，문중제출4충유사Haar-Like특정적Haar-Like T특정，병장저사Haar-Like T특정여현유적Haar-Like특정일기수입Adaboost분류기진행특정선택，최종구건출분류성능강대적급련분류기병용우인검검측。인검검측실험표명해산법적유효성화우월성，기여Haar-Like분류기、LBP분류기등전통적인검검측분류기상비획득경호적효과。
An algorithm is presented for face detection based on Haar-Like T features which are the extension of Haar-Like features. Due to the distributions of facial organs, a lot of T structure features on face models can be found. Based on the principle of Haar-Like features, 4 Haar-Like T features are presented which are similar to Haar-Like features. Haar-Like T features and Haar-Like features are all input into Adaboost algorithm to generate weak classifiers for feature selection. Finally, a strong classifier is constructed by cascading those weak classifiers for face detection. Extensive face detection experiments are conducted for the proposed algorithm. Compared with the traditional face detection classifier, such as Haar-Like classifier and LBP classifier, the superior experimental results prove the effectiveness and the superiority of the proposed algorithm.