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|Other Titles:||Pose Classification of Human Faces in Color Images|
Face Detection;Face Poses Classification;Skin Color Segmentation;Weighting Mask Function
|Issue Date:||2016-09-29 17:02:17 (UTC+8)|
In this paper, we introduce a novel approach for automatic estimation of the poses/degrees of human faces embedded in complicated environments. The proposed system consists of two primary parts. The first part is to search the potential face regions. First, if the input image contains complex background, then the potential face regions are gotten from skin-color- segmentation and the isosceles-triangle criterion that is based on the rules of "the combination of two eyes and one mouth". If the input image contains complex background, then we will use the input RGB color image to perform the human-skin color-segmentation task to remove the complicated surroundings. Then the result of the input image that is removed the complicated surroundings will be converted to a binary image. If the input image doesn't contain complex backgrounds, then we will skip the human-skin color- segmentation task. The input image will be directly converted to a binary image. Secondly, label all 4-connected components and detect any 3 centers of 3 different blocks that form an isosceles triangle. Then, clip the regions that satisfy the isosceles triangle criteria as the potential face regions. The second part of the proposed system is to perform the task of pose verification. In the second part, each face candidate obtained from the previous process is normalized to a standard size (60*60 pixels). Then, each of these normalized potential face regions is fed to the face weighting mask function to obtain the location of the face region. Next, the face region is fed to the direction weighting mask function to determine which direction the matching face region looks at. Last, the face region is fed to the pose weighting mask function to decide the poses/degrees of the human faces. The proposed face poses/degrees classification system can determine the poses of multiple faces embedded in complicated backgrounds. Experimental results demonstrate that an approximately 99% success rate is achieved and the relative false estimation rate is very low.
|Relation:||國立政治大學學報, 83, 197-222|
|Appears in Collections:||[第83期] 期刊論文|
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