中文摘要：

人脸分割对人脸识别、人脸三维建模等人脸图像处理问题具有重要意义,而人脸图像往往轮廓边缘模糊、梯度不明显,常规无边缘几何活动轮廓模型通常无法获得理想的分割效果且计算量较大.为实现快速、准确的人脸轮廓定位及分割,将无边缘几何活动轮廓模型和稀疏场数值算法相结合提出了一个改进的算法,并结合人脸检测和数学形态学算子提出一个基于曲线演化的人脸分割方案.实验结果表明,该算法不仅提高了计算效率,还可以有效地检测出局部模糊或分断边界,进化曲线不会断裂,能够获得较好的人脸分割效果.

英文摘要：

Images containing faces are essential to intelligent vision-based human computer interaction,and research efforts in face processing include face recognition,face tracking,and expression recognition.Many applications assume that the faces in an image or an image sequence have been identified and localized.To build fully automated systems that analyze the information contained in face images,robust and efficient face detection algorithms are required.However,such a problem is challenging because faces are non-rigid and have a high degree of variability in size,shape,color,and texture.The purpose of this paper is to provide a relative robust method for face segmentation in images based on curve evolution methodology.Since the face image always has a blur boundary and little gradient changes,the region segmentations obtained by the original Chan-Vese model are generally unsatisfactory and need large amount of calculations.To achieve more accurate facial contour extraction and face segmentation,a new face segmentation scheme based on curve evolution model is proposed which is a combination of Chan-Vese model,sparse-field algorithm,face detection and mathematical morphology operators.Experimental results show that the improved algorithm can effectively detect the local blur and breaking boundaries on the face images without any fractures in the curve,hence resulting in favorable face segmentations.