基于卷积神经网络的人脸网络的研究与实现

Y. Liu, Jinpeng Ren, Chunya Wang, Xinxin Yuan
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引用次数: 0

摘要

深度学习、人工智能等前沿技术不断融入人们的日常生活。即使是生活中随处可见的小型自动售货机,也开始使用面部支付方式。人脸图像的检测与识别已不再高不可攀,但对人脸信息及特征(性别、年龄、种族等)的分析与识别仍未完全成熟,为了提高人脸信息识别的准确性,本文设计了一个人脸信息识别模型。特征提取部分使用八层卷积神经网络,然后使用两个全连接模块作为分类器进行性别识别和年龄识别。实验结果表明,该模型利用了卷积神经网络的优点,使模型能够更准确地预测人脸的性别和年龄。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research And Implementation Of Facialnet Based On Convolutional Neural Network
Deep learning, artificial intelligence and other cutting-edge technologies are constantly being integrated into people's daily lives. Even small vending machines that can be seen everywhere in life have begun to use facial payment methods. The detection and recognition of face images is no longer unattainable, but the analysis and recognition of face information and characteristics (gender, age, race, etc.) is still not fully mature, in order to improve the accuracy of face information recognition In this paper, a face information recognition model is designed. The feature extraction part uses an eight-layer convolutional neural network, and then uses two fully connected modules as the classifiers for gender recognition and age recognition. The experimental results show that the model uses the advantages of the convolutional neural network so that the model can predict the gender and age of the face more accurately.
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