Face Detection Using Pixel Direction Code and Look-Up Table Classifier

Kil-Taek Lim, Hyunwoo Kang, Byung-Gil Han, J. T. Lee
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引用次数: 0

Abstract

Face detection is essential to the full automation of face image processing application system such as face recognition, facial expression recognition, age estimation and gender identification. It is found that local image features which includes Haar-like, LBP, and MCT and the Adaboost algorithm for classifier combination are very effective for real time face detection. In this paper, we present a face detection method using local pixel direction code(PDC) feature and lookup table classifiers. The proposed PDC feature is much more effective to dectect the faces than the existing local binary structural features such as MCT and LBP. We found that our method’s classification rate as well as detection rate under equal false positive rate are higher than conventional one.
基于像素方向码和查找表分类器的人脸检测
人脸检测是人脸识别、面部表情识别、年龄估计、性别识别等人脸图像处理应用系统全面自动化的基础。研究发现,Haar-like、LBP、MCT等局部图像特征与Adaboost分类器组合算法对于实时人脸检测非常有效。本文提出了一种基于局部像素方向码(PDC)特征和查找表分类器的人脸检测方法。所提出的PDC特征比现有的局部二值结构特征如MCT和LBP更有效地检测人脸。结果表明,该方法在等假阳性率下的分类率和检出率均高于常规方法。
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