R.L. Hsu, M. Abdel-Mottaleb, and A. K. Jain. Face detection in color images. In Proceedings of the IEEE International Conference on Image Processing, pages 1046--1049, 2001.  Home/Search Document Details and Download
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....motion information. 2.1. Face Detection using Motion Guided Template Matching In order to detect a face in a still image, a number of techniques have been reported [1] Feedforward neural networks for classifying face and non face inputs [2] and models based on skin colour have been reported [3, 4]. In our technique, the face is detected in a video sequence using multi scale average face template. The average face template is shown in Figure 1. This was generated by averaging a set of 80 faces that were manually extracted from the FERET face dataset [5] The upper part of the face template ....
R. L. Hsu, M. Abdel-Mottaleb, and A. K. Jain, "Face detection in color images," IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 696--706, May 2002.
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....related skin detection studies. Brown et al. 5] used 2D intensity invariant vectors from each colorspace transformations to train the SelfOrganizing Map (SOM) model. For each of color transformations, 2D intensity invariant vectors were extracted and used to train the SOM model. Hsu et al. [9] used YCbCr colorspace transformation to detect skin pixels in the normalized color appearance using reference white . Skin color was modeled using the Gaussian distribution, then detected by computing Mahalanobis distance. Jones et al. proposed skin non skin models based on distributions in ....
R.L. Hsu, M. Abdel-Mottaleb, and A.K. Jain, "Face Detection in Color Images," IEEE Trans. on Pattern Analysis and Machine Intelligence Vol.24, No.5, pp 696-706, 2002.
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.... coarse alignment, a semantic face graph adapts to a face image through the global and local rigid 3D geometric transformation (scaling, rotation, and translation) based on the detected locations of face and facial components (see Figures 3(a) to 3(d) for detection results using the algorithm in [10]) Currently, we assume that all of the internal and external facial components of a face image are visible to the modeling module. We further employ the edges and color characteristics of facial components to locally refine the rotation, translation, and scaling parameters for individual ....
....i, # i and # are the normal directions of component curve i in a semantic graph G and the gradient orientation of image I, f is the edge magnitude of the image I, and e(u k , v k ) is the facial component map of the image I at pixel k. The gradient magnitude, gradient orientation, eye map (See [10] and Eq. 7) and coarse alignment results for the subject in Fig. 3(c) are shown in Fig. 4. Fig. 4. Boundary map and eye component map for coarse alignment: a) and (b) are gradient magnitude and orientation, respectively, obtained from multi scale Gaussian blurred edge response; c) an eye map ....
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R.-L. Hsu, M. Abdel-Mottaleb, and A. K. Jain, "Face detection in color images," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 5, pp. 696-706, May 2002.
Face Detection in Color Images - Hsu, Abdel-Mottaleb, Jain (2002) (17 citations) Self-citation (Hsu Abdel-mottaleb Jain) (Correct)
....blobs; ii) geometry and orientation of the triangle; and (iii) the presence of a face boundary around the triangle. A face score is computed for each veri ed eye mouth triangle based on its eyes mouth maps, ellipse vote and face orientation that favors upright faces and symmetric facial geometry [16]. The triangle with the highest score that exceeds a threshold is retained. Figure 10 shows the boundary map which is constructed from both the magnitude and the orientation components of the luma gradient within the regions having positive orientations of the gradient orientations (i.e. have ....
R.-L. Hsu, M. Abdel-Mottaleb, and A.K. Jain, "Face detection in color images," Tech. Report MSUCSE -01-7, Michigan State Univ., March 2001.
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....face graphs at the frontal view are reconstructed using Fourier descriptors with spatial frequency components increasing from (a) 10 to (e) 100 . and face matching. The face detection module locates faces (in non profile views) and facial components in a color image using the algorithm in [1]. Figures 4 (a) and (b) show an input color image and the detection results. Currently, we assume that the face images have been captured at near frontal views; we are implementing a pose estimation module. The alignment module uses the face detection results to align a semantic face graph and the ....
R.-L. Hsu, M. Abdel-Mottaleb, and A. K. Jain, "Face detection in color images," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 5, pp. 696--706, May 2002.
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