基于MultiClass-SVM的多核函數(shù)學習在人臉表情識別中應用
發(fā)布時間：2018-11-24 13:42
【摘要】：近年來,人臉表情識別在社交網絡和人機交互領域越來越引起學術界的重視和關注,并且已經取得了一系列的成果�，F(xiàn)有數(shù)據(jù)庫中人臉大多角度端正、分辨率高并且環(huán)境光照良好,并且現(xiàn)有的算法均基于以上數(shù)據(jù)庫設計。而真實世界中的人臉更具有多變性,因此現(xiàn)有的算法很難滿足于實際需求。為測試現(xiàn)有算法的性能,本文探索了一些影響真實生活場景中笑臉檢測的因素,包括光照預處理方法、對齊、圖像尺寸、特征以及SVM分類器核的選取。根據(jù)數(shù)據(jù)本文驗證了現(xiàn)有光照處理方法的局限性,對齊作用的實用性以分類器的核的性能等。同時為了驗證多表情分類問題,本文通過互聯(lián)網搜集并建立了一個有將近3萬張人臉圖像的數(shù)據(jù)庫,Real-world Affective Face Database(RAF-DB),其中每一張人臉圖像的標簽都是被大概40位志愿者進行獨立標注。為了測試本文建立的RAF-DB數(shù)據(jù)庫的性能,引入了CK數(shù)據(jù)庫進行對比,通過交叉訓練,實驗結果表明經過RAF-DB訓練在CK數(shù)據(jù)庫測試的數(shù)據(jù)結果的識別率基本上都高十在CK數(shù)據(jù)庫訓練在RAF-DB數(shù)據(jù)庫測試的結果。搜集的數(shù)據(jù)庫表明人臉表情識別任務是一個典型的非均勻多標簽的分類問題,為了解決上述問題,本文在訓練時,通過上采樣問題進行了數(shù)據(jù)重構,同時也探究了多標簽的影響,試驗結果表明,這對識別率的提高非常明顯。在特征選取方面,除了利用比較成熟的人臉表情特征(HOG,Gabor,LBP)作對比外,還引入了深度學習的特征。對于不同數(shù)據(jù)庫以及分類任務,不同的SVM核的性能差異也非常明顯,因此本文分類器的訓練采用了多核SVM分類器,包括線性核、高斯核以及局部線性核(OCC)。試驗結果表明,多核SVM在表情分類問題上具有更強的穩(wěn)定性和更高的準確率。
[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.
【學位授予單位】：北京郵電大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：TP391.41
本文編號：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.
【學位授予單位】：北京郵電大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：TP391.41
【參考文獻】
相關碩士學位論文 前1條
1 孫雯玉;人臉表情識別算法研究[D];北京交通大學;2006年
，本文編號：2353939
本文鏈接：http://www.sikaile.net/kejilunwen/ruanjiangongchenglunwen/2353939.html
最近更新
教材專著
