基于MultiClass-SVM的多核函数学习在人脸表情识别中应用
发布时间：2018-11-24 13:42
【摘要】：近年来,人脸表情识别在社交网络和人机交互领域越来越引起学术界的重视和关注,并且已经取得了一系列的成果。现有数据库中人脸大多角度端正、分辨率高并且环境光照良好,并且现有的算法均基于以上数据库设计。而真实世界中的人脸更具有多变性,因此现有的算法很难满足于实际需求。为测试现有算法的性能,本文探索了一些影响真实生活场景中笑脸检测的因素,包括光照预处理方法、对齐、图像尺寸、特征以及SVM分类器核的选取。根据数据本文验证了现有光照处理方法的局限性,对齐作用的实用性以分类器的核的性能等。同时为了验证多表情分类问题,本文通过互联网搜集并建立了一个有将近3万张人脸图像的数据库,Real-world Affective Face Database(RAF-DB),其中每一张人脸图像的标签都是被大概40位志愿者进行独立标注。为了测试本文建立的RAF-DB数据库的性能,引入了CK数据库进行对比,通过交叉训练,实验结果表明经过RAF-DB训练在CK数据库测试的数据结果的识别率基本上都高十在CK数据库训练在RAF-DB数据库测试的结果。搜集的数据库表明人脸表情识别任务是一个典型的非均匀多标签的分类问题,为了解决上述问题,本文在训练时,通过上采样问题进行了数据重构,同时也探究了多标签的影响,试验结果表明,这对识别率的提高非常明显。在特征选取方面,除了利用比较成熟的人脸表情特征(HOG,Gabor,LBP)作对比外,还引入了深度学习的特征。对于不同数据库以及分类任务,不同的SVM核的性能差异也非常明显,因此本文分类器的训练采用了多核SVM分类器,包括线性核、高斯核以及局部线性核(OCC)。试验结果表明,多核SVM在表情分类问题上具有更强的稳定性和更高的准确率。
[Abstract]:In recent years, facial expression recognition has attracted more and more attention in the field of social network and human-computer interaction, and has made a series of achievements. In the existing database, the human face is large and multi-angle correct, the resolution is high and the environment illumination is good, and the existing algorithms are based on the above database design. In the real world, human faces are more variable, so the existing algorithms are difficult to meet the actual needs. In order to test the performance of the existing algorithms, this paper explores some factors that affect the detection of smiling faces in real life scenes, including illumination preprocessing, alignment, image size, features and the selection of SVM classifier cores. According to the data, this paper verifies the limitation of existing illumination processing methods and the practicability of alignment to the performance of classifier kernel. At the same time, in order to verify the problem of multi-expression classification, this paper collects and builds a database of nearly 30, 000 face images via the Internet, Real-world Affective Face Database (RAF-DB). Each face image was labeled independently by about 40 volunteers. In order to test the performance of the RAF-DB database established in this paper, the CK database is introduced and compared. The experimental results show that the recognition rate of the data tested in the CK database after RAF-DB training is almost higher than that in the CK database training in the RAF-DB database. The database collected shows that the task of facial expression recognition is a typical non-uniform multi-label classification problem. At the same time, the influence of multi-label is also discussed. The experimental results show that the recognition rate is improved obviously. In feature selection, in addition to using more mature facial expression features (HOG,Gabor,LBP) for comparison, in-depth learning features are also introduced. For different databases and classification tasks, the performance of different SVM kernels is also very different. Therefore, the training of the classifier in this paper uses multi-core SVM classifier, including linear kernel, Gao Si kernel and local linear kernel (OCC). The experimental results show that multicore SVM is more stable and accurate in facial expression classification.
【学位授予单位】：北京邮电大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TP391.41
本文编号：2353939
[Abstract]:In recent years, facial expression recognition has attracted more and more attention in the field of social network and human-computer interaction, and has made a series of achievements. In the existing database, the human face is large and multi-angle correct, the resolution is high and the environment illumination is good, and the existing algorithms are based on the above database design. In the real world, human faces are more variable, so the existing algorithms are difficult to meet the actual needs. In order to test the performance of the existing algorithms, this paper explores some factors that affect the detection of smiling faces in real life scenes, including illumination preprocessing, alignment, image size, features and the selection of SVM classifier cores. According to the data, this paper verifies the limitation of existing illumination processing methods and the practicability of alignment to the performance of classifier kernel. At the same time, in order to verify the problem of multi-expression classification, this paper collects and builds a database of nearly 30, 000 face images via the Internet, Real-world Affective Face Database (RAF-DB). Each face image was labeled independently by about 40 volunteers. In order to test the performance of the RAF-DB database established in this paper, the CK database is introduced and compared. The experimental results show that the recognition rate of the data tested in the CK database after RAF-DB training is almost higher than that in the CK database training in the RAF-DB database. The database collected shows that the task of facial expression recognition is a typical non-uniform multi-label classification problem. At the same time, the influence of multi-label is also discussed. The experimental results show that the recognition rate is improved obviously. In feature selection, in addition to using more mature facial expression features (HOG,Gabor,LBP) for comparison, in-depth learning features are also introduced. For different databases and classification tasks, the performance of different SVM kernels is also very different. Therefore, the training of the classifier in this paper uses multi-core SVM classifier, including linear kernel, Gao Si kernel and local linear kernel (OCC). The experimental results show that multicore SVM is more stable and accurate in facial expression classification.
【学位授予单位】：北京邮电大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TP391.41
【参考文献】
相关硕士学位论文 前1条
1 孙雯玉;人脸表情识别算法研究[D];北京交通大学;2006年
，本文编号：2353939
本文链接：https://www.wllwen.com/kejilunwen/ruanjiangongchenglunwen/2353939.html
