人臉表情圖像特征提取方法研究與實(shí)現(xiàn)
發(fā)布時(shí)間：2019-06-07 17:08
【摘要】：人臉面部表情識別是通過計(jì)算機(jī)對人臉面部由肌肉拉動(dòng)所產(chǎn)生的表情圖像或視頻做特征提取工作,并按照人類目前理解經(jīng)驗(yàn)和思想認(rèn)識來實(shí)施表情歸類和表情識別,從面部信息中提取分析人類情感。表情特征提取的正確性和有用性是表情可否正確識別的關(guān)鍵。本論文的重點(diǎn)是對表情圖像特征提取方法進(jìn)行研究。本論文主要工作具體有以下幾個(gè)方面:首先,詳細(xì)介紹人臉表情識別系統(tǒng)各功能模塊,研究了圖像獲取模塊和預(yù)處理模塊的原理與算法,并進(jìn)行小樣本采集實(shí)驗(yàn),包括以下四個(gè)方面:人臉檢測、圖像灰度化、圖像歸一化、光照補(bǔ)償。其次,對比研究三種常見的表情特征提取算法,包括:局部二值模式(Local Binary Pattern,LBP)、局部相位量化(Local Phase Quantization,LPQ)、旋轉(zhuǎn)不變局部相位量化(Rotation Invariant Local Phase Quantization,RILPQ),在JAFFE圖像庫部分圖像特征提取實(shí)驗(yàn),提取到特征向量灰度圖及量化直方圖做研究比對。本論文在RILPQ算法基礎(chǔ)上,引入二維高斯核方向?qū)?shù),提出一種新的特征提取算法,即:融合高斯導(dǎo)數(shù)RILPQ算法。再次,研究支持向量機(jī)(upport Vector Machine,SVM)理論并運(yùn)用SVM模式識別與回歸的軟件包LIBSVM完成分類識別與回歸。本部分主要研究基于同向高斯核方向?qū)?shù)與RILPQ融合的人臉表情特征提取算法程序設(shè)計(jì),并對三個(gè)參數(shù)做大量實(shí)驗(yàn)研究,包括:方向?qū)?shù)濾波方向、濾波尺度、尺度半徑,尋找到一組最佳實(shí)驗(yàn)參數(shù),表情識別率最高為92.57%。同時(shí),為驗(yàn)證該算法實(shí)驗(yàn)效果,通過運(yùn)行時(shí)間和表情識別率兩項(xiàng)指標(biāo)與前面的三種特征提取算法進(jìn)行比較,證明該算法運(yùn)行時(shí)間較長但是能取得較好的表情識別分類效果。最后,本文又提出了一種異向高斯核方向?qū)?shù)與RILPQ融合的運(yùn)動(dòng)模糊人臉表情特征提取算法。通過JAFFE圖像庫水平方向運(yùn)動(dòng)模糊處理后做特征提取進(jìn)行表情分類。實(shí)驗(yàn)證明:在模糊長度為5像素,尺度半徑為R=9條件下,運(yùn)動(dòng)模糊表情識別率為66.10%,優(yōu)于RILPQ算法識別率1.4個(gè)百分點(diǎn)。
[Abstract]:Facial expression recognition is to extract facial expression images or videos generated by muscle pull by computer, and to classify and recognize facial expressions according to the current understanding experience and ideological understanding of human beings. Extract and analyze human emotion from facial information. The correctness and usefulness of expression feature extraction is the key to the correct recognition of expression. The focus of this paper is to study the method of facial expression image feature extraction. The main work of this paper is as follows: firstly, the functional modules of facial expression recognition system are introduced in detail, the principle and algorithm of image acquisition module and preprocessing module are studied, and the small sample acquisition experiment is carried out. Including the following four aspects: face detection, image grayscale, image normalization, lighting compensation. Secondly, three common expression feature extraction algorithms are compared, including: local binary mode (Local Binary Pattern,LBP), local phase quantification (Local Phase Quantization,LPQ), rotation invariant local phase quantification (Rotation Invariant Local Phase Quantization,RILPQ). In the experiment of image feature extraction in JAFFE image database, the feature vector grayscale map and quantitative histogram are extracted and compared. In this paper, based on the RILPQ algorithm, the directional derivative of two-dimensional Gaussian kernel is introduced, and a new feature extraction algorithm is proposed, that is, the fusion of Gao Si derivative RILPQ algorithm. Thirdly, the theory of support vector machine (upport Vector Machine,SVM) is studied and the classification recognition and regression are completed by using the software package LIBSVM of SVM pattern recognition and regression. In this part, we mainly study the program design of facial expression feature extraction algorithm based on the fusion of Gao Si kernel guide number and RILPQ, and do a lot of experimental research on three parameters, including directional derivative filtering direction, filtering scale, scale radius. A set of optimal experimental parameters were found, and the highest expression recognition rate was 92.57%. At the same time, in order to verify the experimental effect of the algorithm, the running time and expression recognition rate are compared with the previous three feature extraction algorithms, and it is proved that the algorithm has a long running time but can achieve better expression recognition and classification effect. Finally, this paper proposes a motion fuzzy facial expression feature extraction algorithm based on the fusion of abnormal Gao Si kernel guide number and RILPQ. The expression classification is carried out by feature extraction after horizontal motion blur processing in JAFFE image database. The experimental results show that under the condition that the fuzzy length is 5 pixels and the scale radius is R 鈮，
本文編號：2494953
[Abstract]:Facial expression recognition is to extract facial expression images or videos generated by muscle pull by computer, and to classify and recognize facial expressions according to the current understanding experience and ideological understanding of human beings. Extract and analyze human emotion from facial information. The correctness and usefulness of expression feature extraction is the key to the correct recognition of expression. The focus of this paper is to study the method of facial expression image feature extraction. The main work of this paper is as follows: firstly, the functional modules of facial expression recognition system are introduced in detail, the principle and algorithm of image acquisition module and preprocessing module are studied, and the small sample acquisition experiment is carried out. Including the following four aspects: face detection, image grayscale, image normalization, lighting compensation. Secondly, three common expression feature extraction algorithms are compared, including: local binary mode (Local Binary Pattern,LBP), local phase quantification (Local Phase Quantization,LPQ), rotation invariant local phase quantification (Rotation Invariant Local Phase Quantization,RILPQ). In the experiment of image feature extraction in JAFFE image database, the feature vector grayscale map and quantitative histogram are extracted and compared. In this paper, based on the RILPQ algorithm, the directional derivative of two-dimensional Gaussian kernel is introduced, and a new feature extraction algorithm is proposed, that is, the fusion of Gao Si derivative RILPQ algorithm. Thirdly, the theory of support vector machine (upport Vector Machine,SVM) is studied and the classification recognition and regression are completed by using the software package LIBSVM of SVM pattern recognition and regression. In this part, we mainly study the program design of facial expression feature extraction algorithm based on the fusion of Gao Si kernel guide number and RILPQ, and do a lot of experimental research on three parameters, including directional derivative filtering direction, filtering scale, scale radius. A set of optimal experimental parameters were found, and the highest expression recognition rate was 92.57%. At the same time, in order to verify the experimental effect of the algorithm, the running time and expression recognition rate are compared with the previous three feature extraction algorithms, and it is proved that the algorithm has a long running time but can achieve better expression recognition and classification effect. Finally, this paper proposes a motion fuzzy facial expression feature extraction algorithm based on the fusion of abnormal Gao Si kernel guide number and RILPQ. The expression classification is carried out by feature extraction after horizontal motion blur processing in JAFFE image database. The experimental results show that under the condition that the fuzzy length is 5 pixels and the scale radius is R 鈮，
本文編號：2494953
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