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Mail Security Alert Using PCA and Eigen Victor: A Review

机译:使用PCA和EIGEN VICTOR的邮件安全警报:审查

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Real time face recognition has been used to implement a real-time approach for the identification of faces in a security camera induced video stream. Consequently, both facial identification and facial recognition techniques are briefly listed and related technical data are not omitted. The solution proposed was essentially to apply and validate the recognition algorithm Eigenfaces which uses principal component analysis in order to solve the recognition problem of two-dimensional faces. The snapshots reflecting the input images of your device are projected onto a face space (characteristic space) that explains better the variance for the set of facial images. The ‘owners,’ which are the characteristic vectors of the face set, define the face space. These elements help to rebuild a fresh face image that has been projected into face space with considerable details (named weight). To identify the human being, in this feature space the current picture projection is compared to the available projections from the training array. The PCA technique is used since it is effective in developing facial recognition systems. The frame will conduct real-time facial detection and analysis as well as provide feedback in the form of a window showing the details from the database of the topic and send an e-mail to interested organisations.
机译:实时面部识别已被用于实现安全摄像机引起的视频流中面对识别的实时方法。因此,简要列出了面部识别和面部识别技术,并且不省略相关技术数据。提出的解决方案基本上是应用和验证使用主成分分析的识别算法的特征措施,以解决二维面的识别问题。反映设备的输入图像的快照被投影到面部空间(特征空间)上,该方面空间(特征空间)解释了较好的面部图像的差异。 “所有者”是面部集合的特征向量,定义了面部空间。这些元素有助于重建一张已投射到面部空间的新鲜面部图像,其细节(命名为重量)。为了识别人,在该特征空间中,将当前图像投影与来自训练阵列的可用投影进行比较。使用PCA技术,因为它在开发面部识别系统方面是有效的。该框架将进行实时面部检测和分析,并以窗口形式提供反馈,显示来自主题数据库的详细信息,并向感兴趣的组织发送电子邮件。

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