首页> 外国专利> Face Recognition System for Extracting Feature Vector Using Face Recognition Model Based on Deep Learning

Face Recognition System for Extracting Feature Vector Using Face Recognition Model Based on Deep Learning

机译:基于深度学习的人脸识别模型提取特征向量的人脸识别系统

摘要

A face recognition system that extracts feature vectors using a deep learning-based face recognition model according to an aspect of the present invention capable of extracting feature vectors that enable accurate face recognition without being dependent on place or lighting, input data A plurality of face image processing unit for processing the image to generate the output data; And a feature vector generating unit that merges output data output from the last face image processing unit among the plurality of face image processing units into a single layer to generate a predetermined number of feature vectors, and includes one of the plurality of face image processing units. The first face image processing unit inputs a face image as the input image, and the n+1th face image processing unit inputs the nth face image processing unit output data as the input image, and the plurality of face image processing units inputs the input data. A first unit to generate a feature map by applying a convolution filter; A second unit that weights the feature map generated by the first unit; And a calculator configured to add the feature map weighted by the second unit and the input data input to the first unit to generate the output data.
机译:根据本发明的一方面的面部识别系统,其使用基于深度学习的面部识别模型来提取特征向量,该面部识别系统能够提取使得能够进行准确的面部识别而不依赖于位置或光照的特征向量,输入数据多个面部图像处理单元,用于处理图像以生成输出数据;一种特征向量生成单元,其将从多个面部图像处理单元中的最后一个面部图像处理单元输出的输出数据合并为单个层以生成预定数量的特征向量,并且包括多个面部图像处理单元之一。第一面部图像处理单元输入面部图像作为输入图像,并且第n + 1面部图像处理单元输入第n面部图像处理单元输出数据作为输入图像,并且多个面部图像处理单元输入输入数据。 。第一单元通过应用卷积滤波器来生成特征图;第二单元加权由第一单元生成的特征图;以及计算器,被配置为将由第二单元加权的特征图与输入至第一单元的输入数据相加以生成输出数据。

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