基于协同表示的快速人脸识别算法
发布时间：2018-09-01 17:54
【摘要】：人脸识别一直是机器学习领域的热门问题,针对不同场景和不同目标,人们提出了各种解决算法。随着压缩感知理论的发展和成熟,其在人脸识别领域的一项应用就是稀疏表示识别算法,该算法具有对特征提取不敏感和对遮挡物鲁棒性很好的优点。本论文的核心是一种协同表示识别算法,其改进于稀疏表示识别算法,继承了对遮挡物较好的鲁棒性,并大幅提升了算法的速度。针对协同表示识别算法的准确度和实用性,本论文对其进行了三个方面的改进工作:(1)使用多元特征集作为算法模型的输入,并行地训练多个基于不同特征的模型,然后加权求和同一分类下不同特征模型的残差,以此作为识别依据。这种改进方式可以综合利用不同特征从不同角度提取的有效信息,从而提高算法的准确度。(2)给出了一种加权相对距离的指标作为对Outlier情形的判决依据。协同表示识别算法得到的编码稀疏性变弱,因而稀疏表示识别算法中的稀疏集中因子不再适合本算法。加权相对距离指标绕过了对编码稀疏性的依赖,综合考虑了最优解和次优解之间的距离和相似度进行判决,更加适合当前场景。(3)针对实际应用中常见的样本不足的难题,本论文给出了一种基于变换字典的解决方案。通过从标准人脸库中提取不同光照、不同姿态、各种遮挡物等情形下的变换基,生成一个变换字典,扩展和补充当前训练集的不完备字典,从而能够使用少量训练样本就能表示不同场景下的各类人脸。
[Abstract]:Face recognition has always been a hot problem in the field of machine learning. With the development and maturity of compression perception theory, one of its applications in the field of face recognition is sparse representation recognition algorithm, which is insensitive to feature extraction and robust to occlusion. The core of this paper is a collaborative representation recognition algorithm, which is improved in sparse representation recognition algorithm, inheriting better robustness to occlusion, and greatly improving the speed of the algorithm. Aiming at the accuracy and practicability of cooperative representation recognition algorithm, this paper improves it in three aspects: (1) using multivariate feature set as the input of algorithm model, training several models based on different features in parallel. Then weighted sum the residuals of different feature models under the same classification, which is used as the basis of recognition. The improved method can make use of the effective information extracted from different points of view by using different features to improve the accuracy of the algorithm. (2) A weighted relative distance index is given as the basis for judging the Outlier case. The coding sparsity obtained by the cooperative representation recognition algorithm is weak, so the sparse set factor in the sparse representation recognition algorithm is no longer suitable for this algorithm. The weighted relative distance index bypasses the dependence on coding sparsity and synthetically considers the distance and similarity between the optimal solution and the sub-optimal solution, which is more suitable for the current situation. (3) aiming at the problem of lack of samples, which is common in practical application, the weighted relative distance index is more suitable for the current situation. This paper presents a solution based on the transformation dictionary. By extracting the transform bases from the standard face database under different illumination, different posture and various occlusion objects, a transformation dictionary is generated to extend and supplement the incomplete dictionary of the current training set. Thus, a small number of training samples can be used to represent different kinds of faces in different scenes.
【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：2217927
[Abstract]:Face recognition has always been a hot problem in the field of machine learning. With the development and maturity of compression perception theory, one of its applications in the field of face recognition is sparse representation recognition algorithm, which is insensitive to feature extraction and robust to occlusion. The core of this paper is a collaborative representation recognition algorithm, which is improved in sparse representation recognition algorithm, inheriting better robustness to occlusion, and greatly improving the speed of the algorithm. Aiming at the accuracy and practicability of cooperative representation recognition algorithm, this paper improves it in three aspects: (1) using multivariate feature set as the input of algorithm model, training several models based on different features in parallel. Then weighted sum the residuals of different feature models under the same classification, which is used as the basis of recognition. The improved method can make use of the effective information extracted from different points of view by using different features to improve the accuracy of the algorithm. (2) A weighted relative distance index is given as the basis for judging the Outlier case. The coding sparsity obtained by the cooperative representation recognition algorithm is weak, so the sparse set factor in the sparse representation recognition algorithm is no longer suitable for this algorithm. The weighted relative distance index bypasses the dependence on coding sparsity and synthetically considers the distance and similarity between the optimal solution and the sub-optimal solution, which is more suitable for the current situation. (3) aiming at the problem of lack of samples, which is common in practical application, the weighted relative distance index is more suitable for the current situation. This paper presents a solution based on the transformation dictionary. By extracting the transform bases from the standard face database under different illumination, different posture and various occlusion objects, a transformation dictionary is generated to extend and supplement the incomplete dictionary of the current training set. Thus, a small number of training samples can be used to represent different kinds of faces in different scenes.
【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】
相关期刊论文 前1条
1 陈晶;黄曙光;;分布式并行矩阵乘算法分析[J];兵工自动化;2005年05期
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