Scale invariant face detection method using higher-order local autocorrelation features extracted from log-polar image
Scale invariant face detection method using higher-order local autocorrelation features extracted from log-polar image
复制标题
利用对数极坐标图像提取的高阶局部自相关特征的尺度不变人脸检测方法
DOI：
10.1109/afgr.1998.670927
复制
发表时间：
1998
期刊：
影响因子：
--
通讯作者：
T. Mishima
中科院分区：
文献类型：
--
作者：
K. Hotta;Takio Kurita;T. Mishima
本文提出了一种尺度不变的人脸检测方法，该方法将对数极坐标变换图像提取的高阶局部自相关特征与线性判别分析相结合，用于“人脸”和“非人脸”分类。由于对数极图像的HLAC特征对人脸的移动很敏感，我们利用这一特性开发了一种人脸检测方法。从对数极图像中提取的HLAC特征成为尺度和旋转不变量，因为人脸的尺度和旋转表示为对数极图像（坐标）中的移位。将这些特征与扩展到处理“人脸”和“非人脸”类的线性判别分析相结合，可以实现尺度不变的人脸检测系统。
This paper proposes a scale invariant face detection method which combines higher-order local autocorrelation (HLAC) features extracted from a log-polar transformed image with linear discriminant analysis for "face" and "not face" classification. Since HLAC features of log-polar images are sensitive to shifts of a face, we utilize this property and develop a face detection method. HLAC features extracted from a log-polar image become scale and rotation invariant because scalings and rotations of a face are expressed as shifts in a log-polar image (coordinate). By combining these features with the linear discriminant analysis which is extended to treat "face" and "not face" classes, a scale invariant face detection system can be realized.