CAJ | 학술논문

针对真实环境下的多视角人脸检测问题，提出了一种基于差分特征的多视角人脸检测算法。它综合运用2种不同的差分特征：一阶 NPD特征与二阶 Laplace特征，结合 Gentle Adaboost算法与分类回归树(CART)，分别训练基于一阶和二阶差分特征的人脸检测器，再将这2种差分特征的检测结果进行融合，得到最终的人脸检测结果。本文的差分人脸检测器充分利用了2种差分特征的互补性，结合了一阶特征对光照的鲁棒性和二阶特征对旋转的鲁棒性，从而更好地实现了复杂环境下的多视角人脸检测。在 CMU-MIT和 FDDB两大公开人脸检测数据集中对提出的方法进行验证，结果证明了本文提出的差分人脸检测器的有效性，能够较好地检测复杂环境下的多视角人脸。
침대진실배경하적다시각인검검측문제，제출료일충기우차분특정적다시각인검검측산법。타종합운용2충불동적차분특정：일계 NPD특정여이계 Laplace특정，결합 Gentle Adaboost산법여분류회귀수(CART)，분별훈련기우일계화이계차분특정적인검검측기，재장저2충차분특정적검측결과진행융합，득도최종적인검검측결과。본문적차분인검검측기충분이용료2충차분특정적호보성，결합료일계특정대광조적로봉성화이계특정대선전적로봉성，종이경호지실현료복잡배경하적다시각인검검측。재 CMU-MIT화 FDDB량대공개인검검측수거집중대제출적방법진행험증，결과증명료본문제출적차분인검검측기적유효성，능구교호지검측복잡배경하적다시각인검。
Pose and illumination variations are two major challenges in face detection. Therefore,a novel face detection method based on differential features is proposed. This method extracts first order and second order differential features from images, which are respectively used to train two face detectors using the Gentle Adaboost algorithm with the Classification And Regression Trees(CART) as weak classifiers. Given a new image, the two face detectors are first separately applied to detect candidate faces in the image, and then their detected face regions are combined to give the final face detection results. Thanks to the illumination invariance of first order derivative features and to the rotation invariance of second order derivative features, the proposed differential features based face detection method can better handle the detection of multi-view faces in complex background. The proposed method has been evaluated on the CMU-MIT and FDDB datasets and the results demonstrate its effectiveness.