基于曲线表示的人脸表情合成

Facial Expression Synthesis Based on Curve Representation

先将人脸边缘图像分割为曲线段集,通过度量图像在曲线段两侧的局部纹理差异,形成特征序列.在此基础上,比较人脸样本集出现一类特定表情前后的图像,量化曲线段集和特征序列在此类表情运动作用下的变化,找出其中模式,从而在仅有某样本中性表情图像时,根据模式合成出样本在发生某类特定表情变化后的曲线段集和特征序列.运用该方法,可在初始样本有限的情况下,通过合成表情特征有效扩展样本空间,使识别算法更好地适应表情变化.

We proposed a new method to synthesize facial expression.First,we represented face binary edge map with curve segment group.The differences of local texture beside the curve segments were measured,which are considered as the features of the images.Then we measured the deformation of curve segments and the change of the features when the samples in the face database had some kind of expression change,and learned the pattern behind it.Even when we only have the neutral expression sample,following the pattern,we can still synthesize the curve segments and features of the sample for a certain type of expression change.Through this method,the sample space can be effectively extended,and a better performance is given by the recognition algorithm when it faces the challenge of multiple expression change.