人脸表情图像特征提取方法研究与实现
发布时间：2019-06-07 17:08
【摘要】：人脸面部表情识别是通过计算机对人脸面部由肌肉拉动所产生的表情图像或视频做特征提取工作,并按照人类目前理解经验和思想认识来实施表情归类和表情识别,从面部信息中提取分析人类情感。表情特征提取的正确性和有用性是表情可否正确识别的关键。本论文的重点是对表情图像特征提取方法进行研究。本论文主要工作具体有以下几个方面:首先,详细介绍人脸表情识别系统各功能模块,研究了图像获取模块和预处理模块的原理与算法,并进行小样本采集实验,包括以下四个方面:人脸检测、图像灰度化、图像归一化、光照补偿。其次,对比研究三种常见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)、旋转不变局部相位量化(Rotation Invariant Local Phase Quantization,RILPQ),在JAFFE图像库部分图像特征提取实验,提取到特征向量灰度图及量化直方图做研究比对。本论文在RILPQ算法基础上,引入二维高斯核方向导数,提出一种新的特征提取算法,即:融合高斯导数RILPQ算法。再次,研究支持向量机(upport Vector Machine,SVM)理论并运用SVM模式识别与回归的软件包LIBSVM完成分类识别与回归。本部分主要研究基于同向高斯核方向导数与RILPQ融合的人脸表情特征提取算法程序设计,并对三个参数做大量实验研究,包括:方向导数滤波方向、滤波尺度、尺度半径,寻找到一组最佳实验参数,表情识别率最高为92.57%。同时,为验证该算法实验效果,通过运行时间和表情识别率两项指标与前面的三种特征提取算法进行比较,证明该算法运行时间较长但是能取得较好的表情识别分类效果。最后,本文又提出了一种异向高斯核方向导数与RILPQ融合的运动模糊人脸表情特征提取算法。通过JAFFE图像库水平方向运动模糊处理后做特征提取进行表情分类。实验证明:在模糊长度为5像素,尺度半径为R=9条件下,运动模糊表情识别率为66.10%,优于RILPQ算法识别率1.4个百分点。
[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，
本文编号：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，
本文编号：2494953
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