[1]彭程,刘帅师,万川,等.基于局部纹理ASM模型的人脸表情识别[J].智能系统学报,2011,6(3):231-238.
PENG Cheng,LIU Shuaishi,WAN Chuan,et al.An active shape model for facial expression recognition based on a local texture model[J].CAAI Transactions on Intelligent Systems,2011,6(3):231-238.
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PENG Cheng,LIU Shuaishi,WAN Chuan,et al.An active shape model for facial expression recognition based on a local texture model[J].CAAI Transactions on Intelligent Systems,2011,6(3):231-238.
基于局部纹理ASM模型的人脸表情识别
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
6
期数:
2011年第3期
页码:
231-238
栏目:
学术论文—机器感知与模式识别
出版日期:
2011-06-25
- Title:
- An active shape model for facial expression recognition based on a local texture model
- 文章编号:
- 1673-4785(2011)03-0231-08
- 关键词:
- 人脸表情识别; 主动形状模型; 局部纹理模型; RBF神经网络分类器
- Keywords:
- facial expression recognition; active shape model; local texture model; RBF neural network classifier
- 分类号:
- TP391
- 文献标志码:
- A
- 摘要:
- 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.
- Abstract:
- 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.
- 参考文献/References:
-
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备注/Memo
收稿日期： 2010-11-26.
基金项目：吉林省科技发展计划重点资助项目（20071152）;吉林大学“985工程”工程仿生科技创新平台项目资助；吉林大学研究生创新基金资助项目（20101027）.
通信作者：田彦涛.E-mail: tianyt@jlu.edu.cn.
作者简介：
彭程，女，1987年生，硕士研究生，主要研究方向为模式识别、计算机视觉.
刘帅师，女，1981年生，博士研究生，主要研究方向为模式识别、计算机视觉.
万川，男，1985年生，博士研究生，主要研究方向为模式识别、计算机视觉.
田彦涛，男，1958年生，教授、博士生导师、博士.吉林大学自动化研究所所长，中国自动化学会理事，中国自动化学会机器人专业委员会常务委员，吉林省自动化学会理事长，吉林省通信学会副理事长，吉林省电机工程学会常务理事，中科院沈阳自动化研究所先进制造技术实验室学术委员会委员，中国自动化学会《机器人》学报编委，《吉林大学学报（信息科学版）》副主编.主要研究方向为复杂系统建模、优化与控制、机器视觉与模式识别等.近5年，完成国家“863”计划项目1项、国家自然科学基金项目1项、吉林省科技发展计划项目3项、国家“863”智能机器人网点实验室基金项目1项；目前负责承担国家“863”计划项目和国家自然科学基金项目等国家级科研项目3项、吉林省科技发展计划重点项目3项.发表学术论文70余篇.
更新日期/Last Update:
2011-07-23