基于深度学习的人脸识别研究

发布时间：2018-03-12 20:02

本文选题：深度学习　切入点：人脸识别　出处：《大连理工大学》2013年硕士论文　论文类型：学位论文

【摘要】：在实际应用中,如监控系统中,采集到的人脸图像往往是具有多种姿态变化的,并且图像分辨率偏低。受姿态变化和分辨率低的影响,造成的人脸图像识别性能的迅速下降,而姿态变化是人脸识别中一个最为突出的难题。姿态变化将非线性因素引入了人脸识别中。而现有的一些机器学习方法(如只有一个隐层的神经网络、核回归,支撑向量机等)大都使用浅层结构。心理学研究表明对于有限数量的样本和计算单元,浅层结构难以有效地表示复杂函数,并且对于复杂分类问题表现性能及泛化能力针均有明显的不足,尤其当目标对象具有丰富的含义。深度学习可通过学习一种深层非线性网络结构,实现复杂函数逼近,表征输入数据分布式表示,并体现了它对于输入样本数据的强大的本质特征的抽取能力。因此本文运用深度神经网络的方法克服姿态变量和图像分辨率的影响,提出了一种多姿态的人脸超分辨识别算法并在实验数据集上获得了良好的性能表现。另外本文利用深度信念网络探索正面人脸图像和侧面人脸图像的映射,方法放松了深度信念网络的输入也输出之间绝对相等,而只是保证其高层含义上的相等。实验表明了使用深度信念网络可以学习到侧面人脸图像到正面人脸图像的一个全局映射,但是丢失了个体细节差异。本文还提出了基于深度网络保持姿态邻域进行姿态分类的方法,在学习过程中,我们保证了同一个姿态下的人脸图像应该与同一姿态下的多张图像互为邻居。实验证明了,我们的方法在用于姿态分类具有非常良好的性能,但是也发现学习过程中,那些与区别个体的信息逐渐丢失了,这也导致了直接运用非线性近邻元分析的特征的人脸识别的性能不佳。本文是一篇基于深度学习在人脸识别姿态和分辨率问题上的研究,此外,本文还探索了深度信念网络在人脸姿态处理中的潜在应用,如姿态映射和姿态分类。
[Abstract]:In practical applications, such as the monitoring system, the collected face images often have a variety of pose changes, and the image resolution is low, which is affected by the change of the pose and the low resolution, resulting in a rapid decline in the performance of face image recognition. Pose change is one of the most prominent problems in face recognition. Attitude change introduces nonlinear factors into face recognition. Some existing machine learning methods (such as neural network with only one hidden layer, kernel regression, etc.). Psychological studies show that for a limited number of samples and computing units, shallow structures are difficult to represent complex functions effectively. Moreover, the performance and generalization ability of complex classification problems are obviously inadequate, especially if the target object has rich meanings. Depth learning can achieve complex function approximation by learning a deep nonlinear network structure. The distributed representation of input data is characterized by its strong ability to extract essential features of input sample data. Therefore, the influence of attitude variables and image resolution is overcome by using the method of depth neural network. A multi-pose super-resolution face recognition algorithm is proposed and a good performance is obtained on the experimental data set. In addition, this paper uses depth belief network to explore the mapping of frontal face image and side face image. The method relaxes that the input and output of depth belief network are absolutely equal. The experiment shows that the depth belief network can be used to learn a global mapping from the side face image to the frontal face image. However, the differences of individual details are lost. In this paper, we also propose a method of attitude classification based on depth network to maintain attitude neighborhood, in the process of learning, We ensure that the face image in the same pose should be neighbors with multiple images in the same pose. Experiments show that our method has a very good performance in attitude classification, but it is also found in the process of learning. The loss of information from the individual leads to poor performance of face recognition based on the features of nonlinear nearest neighbor element analysis (NNEM). This paper is a study on face recognition pose and resolution based on depth learning. In addition, this paper also explores the potential applications of depth belief network in face pose processing, such as pose mapping and pose classification.
【学位授予单位】：大连理工大学
【学位级别】：硕士
【学位授予年份】：2013
【分类号】：TP391.41

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