基于融合约束局部模型的三维人脸特征点定位

提出基于特征融合约束局部模型的三维人脸特征点定位算法. 该算法对每个特征点分别使用三维网格的深度信息和网格局部形状信息训练分类器，对分类器的响应进行融合. 使用基于融合响应的正则化特征点均值漂移算法进行模型拟合，实现特征点定位. 三维人脸特征点定位经常需要对每个特征点的候选点集进行遍历产生候选点组合，该算法使用模型拟合代替穷举搜索，避免了嵌套循环带来的快速增长的时间开销. 使用FRGC v2.0和Bosphorus数据库，对算法进行实验评估. FRGC v2.0库上的特征点平均误差为2.48~4.12 mm，总体检测成功率为97.3%，其中中性、温和及极端表情下的检测成功率分别为97.6%、97.4%和95.5%. Bosphorus库上3种姿态下的检测成功率分别是94%、95%和89%. 实验结果表明，提出方法具有较好的效果，对表情和小幅度的姿态变化具有较好的鲁棒性.

Feature fusion based constrained local model for three-dimensional facial landmark localization

An algorithm for automatic detection of landmarks on three-dimensional faces was proposed by using a feature fusion based constrained local model. A classifier based on depth information and a classifier based on local shape information of three-dimensional meshes were trained for each landmark. The responses of two classifiers were merged and the regularized landmark mean shift algorithm was applied on fitting for the localization of landmarks. Traversing candidates of each landmark is usually necessary in three-dimensional facial landmark localization to generate candidate combinations. The problem of time overhead for nested loops that increases rapidly by using model fitting instead of exhaustive search was solved. The approach was evaluated based on three-dimensional face databases: FRGC v2.0 and Bosphorus. The mean error of every landmark in the FRGC v2.0 is between 2.48 mm to 4.12 mm. The overall detection success rate is 97.3%, among which 97.6% for neutral expression, 97.4% for mild, 95.5% for extreme. On the Bosphorus database, the success rate of 94%, 95% and 89% was respectively achieved under three different poses. The experimental results show that the proposed approach is comparable to state-of-the-art methods in terms of its accuracy, and good robustness is achieved against expression and small pose variation.