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作 者:郭文强[1] 李梦然 侯勇严[2] 肖秦琨[3] GUO Wenqiang;LI Mengran;HOU Yongyan;XIAO Qinkun(School of Electronic Information and Artificial Intelligence,Shaanxi University of Science&Technology,Xi’an 710021,China;School of Electrical and Control Engineering,Shaanxi University of Science&Technology,Xi’an 710021,China;School of Electronic Information Engineering,Xi’an Technological University,Xi’an 710021,China)
机构地区:[1]陕西科技大学电子信息与人工智能学院,西安710021 [2]陕西科技大学电气与控制工程学院,西安710021 [3]西安工业大学电子信息工程学院,西安710021
出 处:《计算机工程》2021年第4期298-305,312,共9页Computer Engineering
基 金:国家自然科学基金(61271362,62071366);陕西省科技厅重点研发计划(2020SF-286);陕西省教育厅产业化研究项目(18JC003);西安市科技计划(2019216514GXRC001CG002GXYD1.1)。
摘 要:为提高疼痛表情的识别准确性,将约束局部神经域(CLNF)模型和贝叶斯网络(BN)模型相结合,提出一种疼痛表情识别方法。利用CLNF模型获取疼痛表情的关键特征点,在此基础上得到携带大量疼痛信息的面部活动单元(AU),通过对AU加标签处理得到样本数据集。根据定性专家经验获取BN条件概率之间的约束集合,采用变权重方法将样本数据集与约束扩展参数集相融合以完成BN模型的参数估算,并通过BN推理方法实现疼痛表情的最终识别。实验结果表明,与概率潜在语义分析、局部二值体卷积神经网络等方法相比,该方法可有效提高疼痛表情的识别性能,具有更高的识别精度。In order to improve the accuracy of pain expression recognition,this paper proposes a pain expression recognition method that combines the Constrained Local Neural Field(CLNF)model with the Bayesian Network(BN)model.The CLNF model is used to obtain the key feature points of pain expressions and on this basis acquire the facial Activity Unit(AU)with a large amount of pain information.By labeling the AU,the sample data set is obtained.Then the constraint set between BN conditional probabilities is obtained according to the experience of qualitative experts.The variable weight method is used to fuse the sample data set with the constraint extended parameter set to complete the parameter estimation of the BN model,and the BN reasoning method is used to realize the final recognition of pain expression.Experimental results show that compared with Probabilistic Latent Semantic Analysis(PLSA),Local Binary Volume Convolution Neural Network(LBVCNN)and other methods,this method can effectively improve the recognition performance of pain expressions and has higher recognition accuracy.
关 键 词:疼痛表情识别 约束局部神经域模型 活动单元 贝叶斯网络模型 变权重融合 参数估算
分 类 号:TP301.6[自动化与计算机技术—计算机系统结构]
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