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A Face Detection and Recognition System for Color Images Using Neural Networks with Boosting and Deep Learning

机译：基于神经网络的Boosting和深度学习彩色图像人脸检测与识别系统

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摘要

A face detection and recognition system is a biometric identification mechanism which compared to other methods such as finger print identification, speech, signature, hand written and iris recognition, is shown to be more important both theoretically and practically. In principle, the biometric identification methods use a wide range of techniques such as machine learning, computer vision, image processing, pattern recognition and neural networks. The methods have various applications such as in photo and film processing, control access networks, etc. In recent years, the automatic recognition of a human face has become an important problem in pattern recognition. The main reasons are structural similarity of human faces and great impact of illumination conditions, facial expression and face orientation. Face recognition is considered one of the most challenging problems in pattern recognition. A face recognition system consists of two main components, face detection and recognition.;In this dissertation a face detection and recognition system using color images with multiple faces is designed, implemented, and evaluated. In color images, the information of skin color is used in order to distinguish between the skin pixels and non-skin pixels, dividing the image into several components. Neural networks and deep learning methods has been used in order to detect skin pixels in the image. A skin database has been built that contains skin pixels from different human skin colors. Information from different color spaces has been used and applied to neural networks. In order to improve system performance, bootstrapping and parallel neural networks with voting have been used. Deep learning has been used as another method for skin detection and compared to other methods. Experiments have shown that in the case of skin detection, deep learning and neural networks methods produce better results in terms of precision and recall compared to the other methods in this field.;The step after skin detection is to decide which of these components belong to human face. A template based method has been modified in order to detect the faces. The template is rotated and rescaled to match the component and then the correlation between the template and component is calculated, to determine if the component belongs to a human face. The designed algorithm also succeeds if there are more than one face in the component. A rule based method has been designed in order to detect the eyes and lips in the detected components. After detecting the location of eyes and lips in the component, the face can be detected.;After face detection, the faces which were detected in the previous step are to be recognized. Appearance based methods used in this work are one of the most important methods in face recognition due to the robustness of the algorithms to head rotation in the images, noise, low quality images, and other challenges. Different appearance based methods have been designed, implemented and tested. Canonical correlation analysis has been used in order to increase the recognition rate.
机译：人脸检测和识别系统是一种生物识别机制，与其他方法（例如指纹识别，语音，签名，手写和虹膜识别）相比，它在理论上和实践上都显得更为重要。原则上，生物特征识别方法使用多种技术，例如机器学习，计算机视觉，图像处理，模式识别和神经网络。该方法具有各种应用，例如在照相和胶卷处理，控制访问网络等中。近年来，人脸的自动识别已成为模式识别中的重要问题。主要原因是人脸的结构相似性以及照明条件，面部表情和面部朝向的巨大影响。人脸识别被认为是模式识别中最具挑战性的问题之一。人脸识别系统主要由两部分组成：人脸检测和识别。本文设计，实现和评估了一种基于彩色图像的人脸检测和识别系统。在彩色图像中，使用肤色信息来区分皮肤像素和非皮肤像素，并将图像分为几个部分。为了检测图像中的皮肤像素，已经使用了神经网络和深度学习方法。建立了一个皮肤数据库，其中包含来自不同人类肤色的皮肤像素。来自不同颜色空间的信息已被使用并应用于神经网络。为了提高系统性能，已使用具有投票功能的自举和并行神经网络。深度学习已被用作皮肤检测的另一种方法，并与其他方法进行了比较。实验表明，在皮肤检测的情况下，与该领域的其他方法相比，深度学习和神经网络方法在准确性和召回率方面产生了更好的结果。;皮肤检测后的步骤是确定哪些成分属于人脸。基于模板的方法已被修改以检测面部。旋转模板并重新缩放比例以匹配组件，然后计算模板与组件之间的相关性，以确定组件是否属于人脸。如果组件中有多个面，则设计的算法也将成功。设计了一种基于规则的方法，以检测所检测组件中的眼睛和嘴唇。在检测到眼睛和嘴唇在组件中的位置之后，就可以检测到脸部。在检测到脸部之后，将识别在上一步中检测到的脸部。由于算法对图像中头部旋转，噪声，低质量图像和其他挑战的鲁棒性，因此在这项工作中使用的基于外观的方法是面部识别中最重要的方法之一。已经设计，实施和测试了不同的基于外观的方法。为了提高识别率，使用了规范相关分析。

著录项

  • 作者

    Hajiarbabi, Mohammadreza.;

  • 作者单位

    University of Kansas.;

  • 授予单位 University of Kansas.;
  • 学科 Computer science.
  • 学位 Ph.D.
  • 年度 2017
  • 页码 180 p.
  • 总页数 180
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

  • 入库时间 2022-08-17 11:38:52

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