基于改进活动轮廓模型的人脸分割

Face Segmentation Based on Improved Active Contour Model

人脸图像往往轮廓边界模糊、梯度不明显,常规活动轮廓模型通常无法获得理想的分割效果。为实现准确的人脸轮廓定位及分割,结合人脸检测、活动轮廓模型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,并提出一个改进的活动轮廓模型,有效提高了人脸轮廓定位精度和算法收敛速度。实验结果表明该模型可以有效地检测出局部模糊或分断边界而且演化曲线不会断裂,能够获得较好的人脸分割结果;此外,本文提出的C-V模型的窄带实现方法使计算量减少60%。

As the face image always has a blur boundary and little gradient change,the region segmentations obtained by the original active contour model are generally unsatisfactory. 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 face detection,active contour model and mathematical morphology operators. Moreover,an improved active contour model is proposed to increase the accuracy of face contour extraction and speed up the convergence process. Experimental results show that the im- proved active contour model can effectively detect the local blur and breaking boundaries without any fractures in the curve,resulting in a favorable face segmentation. In addition,the improved narrow-band method reduces the comoutation by 60%.