基于局部纹理ASM模型的人脸表情识别

An active shape model for facial expression recognition based on a local texture model

针对主动形状模型(ASM)迭代过程容易陷入局部最优解的不足,提出了一种基于局部纹理模型的改进ASM算法,即EWASM.在局部纹理模型构建中,以每个特征点的中垂线方向搜索其邻域信息以确定最佳匹配位置,对衡量匹配程度的马氏距离加以推广,进而得到改进的扩展加权局部纹理模型,它由中心局部纹理模型、前局部纹理模型和后局部纹理模型共3个子模型加权组成,并对加权参数进行实验优化,使各个特征点之间的联系更加紧密,模型的鲁棒性更好.通过表情识别实验对提出的EWASM算法和传统ASM算法进行对比,选用RBF神经网络分类器进行表情分类,实验结果表明EWASM算法收敛速度更快,识别率也得以提高,并解决了局部最小问题,能更有效地表征表情.

An improved active shape model（ASM） called EWASM （expanded weighted ASM） based on a local texture model was proposed because EWASM overcomes the disadvantage that the active shape model is easy to involve in local optimal solution in the iterative process. In the local texture model, searching adjacent information of each landmark along its perpendicular bisector made the match position best. It improved and promoted Mahalanobis distance which measured the matching degree. Then the local texture model was extended to include the center local texture model, forward local texture model, and backward local texture model. After that, the weighted parameters were optimized experimentally. Thus each landmark is more closely related and the local texture model is more robust. Finally facial expression recognition experiments were conducted comparing EWASM with classical ASM, and a RBF neural network was used as a classification in the expression recognition. Experiments show that the EWASM algorithm solved the local minimum problem and achieved a better convergence rate and recognition effect.