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首页> 外文期刊>International Journal of Computer Science and Engineering >Face Recognition Using Deep Convolutional Network and One-shot Learning
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Face Recognition Using Deep Convolutional Network and One-shot Learning

机译:面部识别使用深度卷积网络和一次射击学习

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Among the most successful application of images analysis and understanding, face recognition has recently received significant attention, especially during the past few years. Facial recognition technology (FRT) has emerged as an attractive solution to address many contemporary needs for identity and verification of identity claims. Face recognition is the identification of humans by the unique characteristics of their faces. FRT technology is the least intrusive and fastest bio-metric technology. It works with the most obvious individual identifier for the human face. With increasing security needs and with the advancement in technology extracting information has become much simpler. The system proposed in this paper uses the power of Convolution Neural Network (CNN) to encode the face and produce a vector matrix. Then we use tripled loss function to calculate the distance between input and trained image to predict the face.
机译:在图像分析和理解的最成功应用中,人脸识别最近受到了重大关注,特别是在过去几年中。面部识别技术(FRT)已成为一种有吸引力的解决方案,以满足许多当代对身份和认同索赔的验证的当代需求。面部识别是通过脸部的独特特征来识别人类。 FRT技术是最不侵入性和最快的生物公制技术。它适用于人类脸部最明显的个人标识符。随着安全需求的增加,技术提升信息的进步已经变得更加简单。本文提出的系统使用卷积神经网络(CNN)的功率来编码面部并产生矢量矩阵。然后我们使用三倍损耗函数来计算输入和训练图像之间的距离以预测面部。

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