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计算机系统应用:2019,28(2):196-200
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基于分块CBP特征和稀疏表示的三维人脸表情识别
马杰, 蔡轶珩, 盛楠
(北京工业大学 信息学部, 北京 100124)
3D Facial Expression Recognition Using Block CBP Features and Sparse Representation
MA Jie, CAI Yi-Heng, SHENG Nan
(Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China)
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投稿时间:2018-05-22    修订日期:2018-06-15
中文摘要: 在三维人脸表情识别中,基于局部二值模式(LBP)算子算法与传统的特征提取算法相比具有特征提取准确、精细、光照不变性等优点,但也有直方图维数高、判别能力差、冗余信息大的缺点.本文提出一种通过对整幅图像进行多尺度分块提取CBP特征的CBP算法,能够更有效的提取分类特征.再结合使用稀疏表达分类器实现对特征进行分类和识别.经实验结果表明,与传统LBP算法和SVM分类识别算法对比,文中算法用于人脸表情的识别的识别率得到大幅度提高.
中文关键词: LBP  CBP  特征提取  稀疏表  特征分块提取
Abstract:In 3D facial expression recognition, operator algorithm based on LBP has the characteristics of accurate, precise, and invariable illumination, while it also has the disadvantages of high dimensions of histogram, poor discriminating ability, and large redundant information, comparing with traditional feature extraction method. In this study, a CPB algorithm is proposed based on multi-scaled block CBP feature extraction system for the classification of facial expressions that are represented in 3D, designating to extract features more efficiently. Then, sparse expression classifier is used to classify and identify the features. The experimental results demonstrate that the facial expression recognition rate has been greatly improved, comparing with traditional LBP algorithm and SVM algorithm.
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基金项目:国家重点研发计划课题(SQ2017YFC170323);北京市教委科技项目(2017YFC1703302)
引用文本:
马杰,蔡轶珩,盛楠.基于分块CBP特征和稀疏表示的三维人脸表情识别.计算机系统应用,2019,28(2):196-200
MA Jie,CAI Yi-Heng,SHENG Nan.3D Facial Expression Recognition Using Block CBP Features and Sparse Representation.COMPUTER SYSTEMS APPLICATIONS,2019,28(2):196-200

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