一种基于稀疏编码的人脸特征点检测方法

A Sparse Coding-based Method for Facial Feature Point Detection

为了能较好地检测遮挡的人脸形状,提出了一种基于稀疏编码的人脸特征点检测方法.在该方法中,采用基于级联回归的思想,用基于稀疏约束的重构模型来迭代搜索人脸特征点位置.首先,利用训练集中的图像学习一个具有全局性以及通用性为特点的稀疏字典,然后结合人脸特征点局部区域内的纹理信息,使用支持向量回归的方法,重构出每次迭代所需的差分形状.为了验证该方法的可行性,将提出的方法在三个公开的数据集上进行测试.进而,为了进一步验证该方法的有效性,将该方法与两个性能较好的方法进行比较.实验结果表明所提方法具有可行性,且对遮挡的人脸特征点具有较高的识别率.

In order to better detect the occlusion of the face shape, this paper propose a sparse coding-based method for facial feature point detection. This method adopts the idea of cascade regression, and uses the reconstruction model based on sparse constraint to iteratively find the positions of facial feature point. Firstly, a sparse dictionary is obtained during training stage, which is global and universal. Then, using the support vector regression and combining the texture information in the local region of the facial feature points, the method can reconstruct differential shape for each iteration. In order to verify the feasibility and effectiveness, this method is tested on three public face datasets, and compares with two methods. The experimental results show that our proposed method is feasible and the recognition rate of occluded face feature points is high.