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Scale invariant face detection method using higher-order local autocorrelation features extracted from log-polar image

机译:使用从日志极性图像中提取的高阶本地自相关特征的缩放不变性面部检测方法

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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.
机译:本文提出了一种规模不变的面部检测方法,它结合了从逻辑极化变换图像中提取的高阶局部自相关(HLAC)特征,以与“面部”和“不面对”分类的线性判别分析。由于Log-Polar图像的HLAC特征对面部的偏移敏感,因此我们利用此属性并开发面部检测方法。从日志极性图像中提取的HLAC功能变为刻度和旋转不变,因为面部的缩放和旋转表示为逻辑极性图像(坐标)中的移位。通过将这些特征与延伸以处理“面部”和“不面对”类的线性判别分析,可以实现规模不变的面部检测系统。

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