英语论文网

人脸侦测技术广泛的运用范围，吸引了大批研究者。在给出一张脸相之后，如果需要的话，人脸侦测技术可以对人脸进行局部化监测。人脸侦测是所有自动化系统运转的第一步，这些系统主要是解决以下问题：脸部识别，脸部追踪和面部表情识别。过去十年里，安全系统以用户身份信息为基础，例如指纹和声音识别，而现在它依赖的则是脸部特征识别。通过使用脸部识别技巧，对图像的特征进行提取和分析，安全系统连接的计算机会做出相应的反应。人脸识别侦测技术目前是计算机视觉研究中比较活跃的热门领域，其局部化和识别通常是录像监控、人机接口、面貌识别和影响数据库管理的首要步骤。尽管人们会假定标准化脸部图像是易获取的，对人脸的定位和追踪依然是脸部识别或表情分析的必备条件。为了对一张人脸进行定位，需要使用照相机或是扫描抓帧器来跟进图像，对其重要特征进行搜索，最后运用这些特征来定位。脸部识别技术被划分为三大类，即，以知识为基础，以图像为基础，以特征为基础。以知识为基础的识别依赖于人类面部特征的一般规则。比如人脸的双眼呈对称分布，一个鼻子，一个嘴巴，部位的相对距离也能够代表一定的关系。在识别出特征之后，要进行验证过程以减少误断。

Face detection has attracted many researchers because it has a wide area of applications. Given an image, face detection involves localizing all faces - if any - in this image. Face detection is the first step in any automated system that solves problems such as: face recognition, face tracking, and facial expression recognition [1]. In the past decade, the security systems based on the information about a user’s identity, like fingerprint and voice recognition, but now it depends on facial feature recognition. The extraction of these features from images is done by using face recognition techniques, and that computers of the security systems can then react accordingly. Human face detection is currently an active research area in the computer vision community. Human face localization and detection is often the first step in applications such as video surveillance, human computer interface, and face recognition and image database management. Locating and tracking human faces is a prerequisite for face recognition or facial expressions analysis, although it is often assumed that a normalized face image is available. In order to locate a human face, the system needs to capture an image using a camera and a frame-grabber to process the image, search the image for important features and then use these features to determine the location of the face. The techniques for face detection were classified into three categories i.e. Knowledge based, Image based and Feature based. The knowledge based approach depends on using rules about human facial features to detect faces. Human facial features for examples as two eyes that are symmetric to each other, a nose and mouth, and features’ relative distances represent features relationships. After detecting features, a verification process is done to reduce false detection. This approach is good for frontal images. The difficulty of it is to translate human knowledge into known rules and to detect faces in different poses. In Image based technique there is a predefined standard face pattern is used to match with the segments in the image to determine whether they are faces or not. It uses training algorithms to classify regions into face or non-face classes. Image-based techniques depends on multi-resolution window scanning to detect faces, so these techniques have high detection rates but slower than the feature-based techniques and lastly Feature based technique depends on extraction of facial features that are not affected by variations in lighting conditions, pose, and other factors. These methods are classified according to the extracted features. Feature-based techniques depend on feature derivation and analysis to gain the required knowledge about faces. Features may b本论文由英语论文网提供整理，提供论文代写，英语论文代写，代写论文，代写英语论文，代写留学生论文，代写英文论文，留学生论文代写相关核心关键词搜索。