基于低秩稀疏的人脸表情识别方法研究
发布时间：2018-08-28 13:31
【摘要】：人脸表情是日常交流的主要方式之一,相对其他表达方式而言,能更有效的体现彼此的内心活动。人脸表情识别涵盖了心理学、生理学、图像处理、模式识别等多个领域,是一个交叉性的学科。在人机交互领域有着广泛的运用,但由于相关的识别技术还不成熟,在日常的生活运用还处在一个尝试性的阶段,存在识别率不高等问题,因此值得进一步深入研究。本文主要的研究内容如下:1.针对协作低秩分层稀疏表情识别算法采用随机取样的方式构建表情字典,导致表情识别效果并不稳定。因此可以通过结合LC-KSVD(Label consist K-SVD)字典学习方法,提高协作低秩分层稀疏表情识别算法的稳定性和准确度。2.由于LC-KSVD算法在训练字典的时候,受到最后一次训练样本的影响更大,并且字典原子间极有可能存在较大相关性,特别是当字典规模较小时,不能学习出有效字典,影响着识别的准确度。但如果当字典规模较大时,算法成本又将加大。因此需要设计出一个尺度自适应的,且各原子间相干性最低的字典学习算法,使得字典能够以最合适的字典规模,包含更有效的分类信息。3.基于低秩稀疏的人脸表情识别方法一般通过有效的分离表情变化稀疏矩阵,然后在特定表情字典上对该稀疏矩阵进行稀疏表示,以达到最佳的识别效果。但实际运用中往往受到一些噪声干扰,使得相应的低秩稀疏分解算法不能有效的分离表情变化稀疏部分,使得低秩稀疏分解算法在实际的人脸表情识别运用中存在不少的缺陷。因此通过添加相关约束项,可以将复杂噪声从表情序列中分离,并有效的提取表情变化特征,从而提高识别效率。
[Abstract]:Facial expression is one of the main ways of daily communication. Compared with other expressions, facial expression can more effectively reflect each other's inner activities. Facial expression recognition covers many fields, such as psychology, physiology, image processing, pattern recognition and so on. It is widely used in the field of human-computer interaction, but because the related recognition technology is not mature, the daily life of the application is still in a trial stage, there are problems such as low recognition rate, so it is worth further study. The main contents of this paper are as follows: 1. An expression dictionary is constructed by random sampling for collaborative low rank hierarchical sparse expression recognition algorithm, which results in unstable performance of expression recognition. Therefore, we can improve the stability and accuracy of the collaborative low rank hierarchical sparse expression recognition algorithm by combining the LC-KSVD (Label consist K-SVD) dictionary learning method. Because the LC-KSVD algorithm is more affected by the last training sample when training the dictionary, and the dictionary atoms are likely to have a greater correlation, especially when the dictionary size is small, it can not learn an effective dictionary. It affects the accuracy of recognition. But if the dictionary is large, the cost of the algorithm will increase. Therefore, it is necessary to design an adaptive dictionary learning algorithm with the lowest coherence among atoms, so that the dictionary can contain more effective classification information with the most appropriate dictionary size. The low rank sparse facial expression recognition method usually separates the sparse matrix of expression change effectively and then sparse represents the sparse matrix in a specific expression dictionary in order to achieve the best recognition effect. However, in the practical application, some noises often interfere, which makes the corresponding low-rank sparse decomposition algorithm can not effectively separate the sparse parts of facial expression changes, which makes the low-rank sparse decomposition algorithm have many defects in the actual application of facial expression recognition. Therefore, the complex noise can be separated from the expression sequence by adding correlation constraints, and the feature of facial expression change can be extracted effectively, so as to improve the recognition efficiency.
【学位授予单位】：昆明理工大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP391.41
本文编号：2209532
[Abstract]:Facial expression is one of the main ways of daily communication. Compared with other expressions, facial expression can more effectively reflect each other's inner activities. Facial expression recognition covers many fields, such as psychology, physiology, image processing, pattern recognition and so on. It is widely used in the field of human-computer interaction, but because the related recognition technology is not mature, the daily life of the application is still in a trial stage, there are problems such as low recognition rate, so it is worth further study. The main contents of this paper are as follows: 1. An expression dictionary is constructed by random sampling for collaborative low rank hierarchical sparse expression recognition algorithm, which results in unstable performance of expression recognition. Therefore, we can improve the stability and accuracy of the collaborative low rank hierarchical sparse expression recognition algorithm by combining the LC-KSVD (Label consist K-SVD) dictionary learning method. Because the LC-KSVD algorithm is more affected by the last training sample when training the dictionary, and the dictionary atoms are likely to have a greater correlation, especially when the dictionary size is small, it can not learn an effective dictionary. It affects the accuracy of recognition. But if the dictionary is large, the cost of the algorithm will increase. Therefore, it is necessary to design an adaptive dictionary learning algorithm with the lowest coherence among atoms, so that the dictionary can contain more effective classification information with the most appropriate dictionary size. The low rank sparse facial expression recognition method usually separates the sparse matrix of expression change effectively and then sparse represents the sparse matrix in a specific expression dictionary in order to achieve the best recognition effect. However, in the practical application, some noises often interfere, which makes the corresponding low-rank sparse decomposition algorithm can not effectively separate the sparse parts of facial expression changes, which makes the low-rank sparse decomposition algorithm have many defects in the actual application of facial expression recognition. Therefore, the complex noise can be separated from the expression sequence by adding correlation constraints, and the feature of facial expression change can be extracted effectively, so as to improve the recognition efficiency.
【学位授予单位】：昆明理工大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP391.41
【参考文献】
相关期刊论文 前6条
1 杨凡;张磊;;基于Gabor参数矩阵与改进Adaboost的人脸表情识别[J];计算机应用;2014年04期
2 唐恒亮;孙艳丰;朱杰;赵明茹;;融合LBP和局部稀疏表示的人脸表情识别[J];计算机工程与应用;2014年15期
3 周晓彦;郑文明;辛明海;;基于稀疏表示的KCCA方法及在表情识别中的应用[J];模式识别与人工智能;2013年07期
4 王志良,陈锋军,薛为民;人脸表情识别方法综述[J];计算机应用与软件;2003年12期
5 金辉,高文;人脸面部混合表情识别系统[J];计算机学报;2000年06期
6 高文，金辉;面部表情图像的分析与识别[J];计算机学报;1997年09期
相关博士学位论文 前1条
1 程广涛;基于压缩感知的人脸识别方法研究[D];天津大学;2015年
，本文编号：2209532
本文链接：https://www.wllwen.com/kejilunwen/ruanjiangongchenglunwen/2209532.html
