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Fusion of visible and thermal images for facial expression recognition

Frontiers of Computer Science 2014年第2期8卷 232-242页

作者： Shangfei WANG Shan HE Yue WU Menghua HE Qiang JI School of Computer Science and Technology University of Science and Technology of China Hefei 230027 China Key Lab of Computing and Communicating Software of Anhui Province Hefei 230027 China Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute Troy NY12180-3590 USA

Most present research into facial expression recognition focuses on the visible spectrum, which is sen- sitive to illumination change. In this paper, we focus on in- tegrating thermal infrared data with visible spectrum images for spontaneous facial expression recognition. First, the ac- tive appearance model AAM parameters and three defined head motion features are extracted from visible spectrum im- ages, and several thermal statistical features are extracted from infrared （IR） images. Second, feature selection is per- formed using the F-test statistic. Third, Bayesian networks BNs and support vector machines SVMs are proposed for both decision-level and feature-level fusion. Experiments on the natural visible and infrared facial expression （NVIE） spontaneous database show the effectiveness of the proposed methods, and demonstrate thermal 1R images＇ supplementary role for visible facial expression recognition.

关键词： facial expression recognition feature-level fu-sion decision-level fusion support vector machine bayesiannetwork thermal infrared images visible spectrum images

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Online Markov Blanket Learning with Group Structure

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Intelligent Automation & Soft Computing 2023年第7期37卷 33-48页

作者： Bo Li Zhaolong Ling Yiwen Zhang Yong Zhou Yimin Hu Haifeng Ling School of Computer Science and Technology Anhui UniversityHefeiAnhui Province230601China Institute of Intelligent Machines Hefei Institutes of Physical ScienceChinese Academy of SciencesHefeiAnhui Province230031China School of Management Hefei University of TechnologyHefeiAnhui Province230009China

Learning the Markov blanket(MB)of a given variable has received increasing attention in recent years because the MB of a variable predicts its local causal relationship with other *** MB Learning can learn MB for a given variable on the ***,in some application scenarios,such as image analysis and spam filtering,features may arrive by *** online MB learning algorithms evaluate features individually,ignoring group *** by this,we formulate the group MB learning with streaming features problem,and propose an Online MB learning with Group Structure algorithm,OMBGS,to identify the MB of a class variable within any feature group and under current feature space on the *** experiments on benchmark Bayesian network datasets demonstrate that the proposed algorithm outperforms the state-of-the-art standard and online MB learning algorithms.

关键词： Markovblanket bayesiannetwork streamingfeatures

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