论文标题
使用与Delaunay Triangulation集成的PCA的面部识别
Face recognition using PCA integrated with Delaunay triangulation
论文作者
论文摘要
面部识别最用于生物识别用户身份验证，该验证根据用户的面部功能识别用户。该系统的需求量很高，因为它被许多企业使用并在许多设备（例如智能手机和监视摄像头）中使用。但是，在此用户验证方法中仍观察到的一个常见问题是其精度率。已经对许多方法和算法进行了实验，以改善系统的规定缺陷。这项研究开发了一种使用两种不同方法组合的算法。使用线性代数和计算几何形状的概念，研究研究了主成分分析与Delaunay三角测量的整合。该方法三角形调节一组面部标记点，并获得了提供的图像的特征。它将算法与传统PCA进行比较，并讨论了包含不同面部标记点以提供有效的识别率。
Face Recognition is most used for biometric user authentication that identifies a user based on his or her facial features. The system is in high demand, as it is used by many businesses and employed in many devices such as smartphones and surveillance cameras. However, one frequent problem that is still observed in this user-verification method is its accuracy rate. Numerous approaches and algorithms have been experimented to improve the stated flaw of the system. This research develops one such algorithm that utilizes a combination of two different approaches. Using the concepts from Linear Algebra and computational geometry, the research examines the integration of Principal Component Analysis with Delaunay Triangulation; the method triangulates a set of face landmark points and obtains eigenfaces of the provided images. It compares the algorithm with traditional PCA and discusses the inclusion of different face landmark points to deliver an effective recognition rate.
