从自然背景中提取多人的人脸进行个人识别

M. Ikeda, H. Ebine, O. Nakamura
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引用次数: 15

摘要

一般来说,从自然背景中提取人脸是非常困难的。提出了一种基于自然背景的多人面部区域的人脸提取方法。该过程利用人体皮肤颜色和运动信息来执行,包括以下五个阶段:(1)运动区域的检测;(2)肤色区域提取;(3)评价各肤色区域的运动;(4)运动较大时,从每个肤色区域提取面部区域;(5)运动较小时,从每个肤色区域提取面部区域。首先使用两个连续帧之间的差来检测移动区域。然后基于改进的HSV颜色系统从输入图像中提取肤色区域。判断每个肤色区域的运动是否大。面部区域的提取过程根据运动量分为两个过程:(1)当运动量较大时,根据其运动信息提取包含颈部区域的面部区域;(2)当运动较小时,由于连续帧之间面部区域的形状不会发生变化,因此在前一帧中已经提取的面部区域的基础上裁剪出面部区域。检测从前一帧中提取的与面部区域相对应的肤色区域。使用1563帧进行计算机仿真,提取精度达到89%以上。从实验结果来看,该方法在人脸识别、图像编码或面部表情识别等方面的应用前景十分乐观。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Extraction of faces of more than one person from natural background for personal identification
In general, it is very difficult to extract human faces from a natural background. In this paper, the extracting method of faces from more than one person's facial area with a natural background is presented. The processes are executed by using human skin color and moving information, and consist of the following five stages: (1) detection of moving area; (2) extraction for skin-color areas; (3) evaluation of movement of each skin-color area; (4) extraction of facial areas from each skin-color area when movement is large; and (5) the extraction of facial areas from each skin-color area when movement is small. The moving area is first detected using the difference between two successive frames. The skin-color areas are then extracted based on the modified HSV color system from an input image. The judgment whether the movement of each skin-color area is large or not is done. The extracting process of the facial area is divided into two processes according to the moving quantity: (1) when the movement is large, the facial areas containing a neck area are extracted based on its motion information; and (2) when the movement is small, since the shape of the facial areas does not change between the successive frames, the facial areas are then clipped out based on the facial area that has already been extracted in the previous frame. The skin-color area corresponding to the facial area which is extracted from the previous frame is detected. Computer simulation using 1563 frames shows more than 89% extracting accuracy. From the experimental results, the prospects of using this method for the human face identification, image coding or the recognition of facial expressions are very encouraging.
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