基于人脸识别的出入口人物身份识别

发布时间：2018-03-09 04:14

本文选题：人脸定位　切入点：人脸识别　出处：《贵州民族大学》2017年硕士论文　论文类型：学位论文

【摘要】：为了方便出入口人物身份的识别,减轻安保人员值班的压力,前人在视频监控领域对出入口人物身份识别系统进行了大量探索,取得不少的成果,然而仍有一些问题值得进一步研究,例如:如何在人脸定位算法获取定位结果的基础上再次运用其它定位算法来确保定位的精度;怎样在人脸识别精度与识别速度上做取舍;如何能隐性自动提取图像特征。自适应增强算法(Adaptive Boosting Algorithm,Adaboost算法)是常用的人脸定位算法,卷积神经网络(Convolutional Neural Network,CNN)是当今深度学习的关键技术之一。本文在前人工作基础上,采用Adaboost算法与CNN相结合开发出入口人物身份识别系统,本系统主要功能是对出入口行人进行实时定位、特征提取、人物身份识别,实时掌握进入出入口人员身份,当系统发现有外部人员出入时,本系统自动警示值班安保人员注意。本文的开发工作主要分为三个方面:1.使用高斯混合模型提取感兴趣的运动目标区域,为下一步的人脸定位有效地缩小了检测范围。2.提出一种结合Adaboost算法与CNN的人脸定位方法。该方法首先在目标区域内使用Adaboost算法寻找人脸区域;然后,把人脸图像区域读进CNN里再次判别,这样可在Adaboost算法获取定位结果的基础上进一步提高人脸定位精度。3.提出一种采用步长为2的方法进行计算卷积层;减少网络采样层,在损失一定的识别精度的前提下,减少了人脸识别时间。将上述3点集成系统应用于视频监控的出入口场景中,并对出入口人员进行身份识别。本文测试工作主要从人脸定位、人脸识别、系统实地场景三个方面进行测试:1.在美国加州理工学院吴恩达等人建立的人脸定位数据库和实地场景进行人脸定位实验表明,采用Adaboost算法与CNN相结合的方法在人脸定位精度上高于单纯采用Adaboost的方法。2.在人脸识别公开库yale与ORL测试实验结果表明,改进的CNN算法虽然牺牲了一定的识别精度,但在识别时间上平均每张图像识别时间减少了0.013秒。3.在贵州民族大学办公楼、实验楼、宿舍楼、幼儿园出入口实地场景进行测试。本系统在CNN训练过程时直接用原始图像来训练,隐性地自动提取出图像信息特征并分类,在人脸定位方面使用Adaboost算法和CNN相结合的方法有效剔除伪人脸图像,为下一步的工作创造条件;而人脸识别方面采用改进的CNN算法,在牺牲一定的识别精度的前提下,减少了识别时间,为实时识别打下基础,该系统可以减轻安保人员的工作压力和保障人们的人身及财产安全。因此,具有一定的应用价值与推广价值。
[Abstract]:In order to facilitate the identification of people at the entrance and exit, and to alleviate the pressure on security personnel on duty, the predecessors have made a great deal of exploration in the field of video surveillance and achieved a lot of results. However, there are still some problems worthy of further study, such as: how to use other localization algorithms to ensure the location accuracy again on the basis of the face localization algorithm to obtain the localization results, how to make a choice between the face recognition accuracy and the recognition speed, and how to make a choice between the face recognition accuracy and the recognition speed. Adaptive Boosting algorithm (Adaboost) is one of the most popular facial localization algorithms, and Convolutional Neural Network (CNN) is one of the key techniques of deep learning. The main function of this system is to locate the entrance pedestrian in real time, extract the feature, recognize the character identity, and master the identity of the person entering the entrance and exit in real time, by using Adaboost algorithm and CNN, and the main function of the system is to locate the pedestrian in real time. When the system detects the external personnel, the system automatically warns the security personnel on duty. The development work of this paper is divided into three aspects: 1.Using Gao Si mixed model to extract the moving target area of interest. For the next step, the detection range of face location is reduced effectively. 2. A face localization method combining Adaboost algorithm with CNN is proposed. Firstly, the Adaboost algorithm is used to find the face region in the target area. The face image region can be read into CNN again, so that the accuracy of face location can be further improved on the basis of Adaboost algorithm. 3. A method with step size of 2 to calculate convolutional layer is proposed to reduce the network sampling layer. On the premise of losing certain recognition accuracy, the time of face recognition is reduced. The above three-point integrated system is applied to the entrance scene of video surveillance, and the identification of the entrance and exit personnel is carried out. Face recognition and system field scene are tested in three aspects: 1.The experiments of human face location database and field scene established by Wu Enda and others of California Institute of Technology, USA show that, The combination of Adaboost algorithm and CNN method is more accurate than Adaboost method. The experimental results of yale and ORL in the face recognition open library show that the improved CNN algorithm sacrifices some recognition accuracy. However, the average recognition time per image was reduced by 0.013 seconds. 3. In the office building, experimental building, dormitory building of Guizhou University for nationalities, In the process of CNN training, the system directly uses the original image to train, and recessive automatically extracts the image information features and classifies them. In the aspect of face location, the method of combining Adaboost algorithm with CNN algorithm is used to eliminate the pseudo-face image effectively, which creates the conditions for the next step, while the improved CNN algorithm is used in face recognition, at the premise of sacrificing certain recognition accuracy. It can reduce the identification time and lay the foundation for real-time recognition. The system can reduce the working pressure of security personnel and protect the personal and property safety of people, so it has certain application value and popularization value.
【学位授予单位】：贵州民族大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP391.41

【参考文献】