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Face Detection by Discrete Gabor Jets and Reference Graph of Fiducial Points

机译:离散Gabor射流的人脸检测和基准点参考图

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A novel face detection scheme is described. The facial feature extraction algorithm is based on discrete approximation of Gabor Transform, called Discrete Gabor Jets (DGJ), evaluated in fiducial face points. DGJ is computed using integral image for fast summations in arbitrary windows, and by FFT operations on short contrast signals. Contrasting is performed along radial directions while frequency analysis along angular direction. Fourier coefficients for a small number rings create a long vector which is next reduced to few LDA components. Four fiducial points are only considered: two eye corners and two nose corners. Fiducial points detection is based on faceonface classifier using distance to point dependent LDA center and threshold corresponding to equal error rate on ROC. Finally, the reference graph is used to detect the whole face. The proposed method is compared with the popular AdaBoost technique and its advantages and disadvantages axe discussed.
机译:描述了一种新颖的面部检测方案。面部特征提取算法基于Gabor变换的离散近似,称为离散Gabor射流(DGJ),在基准面部点中进行了评估。 DGJ使用积分图像在任意窗口中进行快速求和,并通过对短对比度信号进行FFT运算来计算。沿径向进行对比,而沿角度方向进行频率分析。少量环的傅立叶系数会创建一个长向量,然后将其减少为几个LDA分量。仅考虑四个基准点:两个眼角和两个鼻角。基准点检测基于人脸/非人脸分类器,使用到点依赖的LDA中心的距离和与ROC上相等错误率相对应的阈值。最后,参考图用于检测整个脸部。将该方法与流行的AdaBoost技术进行了比较,并讨论了其优缺点。

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