基于深度模型的属性学习在人脸验证中的应用研究
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引用本文:刘 程,谭晓阳.基于深度模型的属性学习在人脸验证中的应用研究[J].计算技术与自动化,2018,(3):111-114
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刘 程,谭晓阳 (南京航空航天大学 计算机科学与技术学院江苏 南京210016) 
中文摘要:人脸属性,如性别,年龄等对于特征人脸的构成具有唯一性。针对传统人脸验证方法的研究,提出了一种基于深度模型的属性预测方法。该方法是基于深度卷积神经网络模型提取的人脸特征表示,通过标记属性信息的数据训练分类器进行属性预测,并将其用于人脸验证环节以提高验证准确率。该方法提供了一种从深度模型提取的人脸特征表示中分析人脸属性的思路,实验证明,该方法在实际应用中能够有效提高人脸验证的准确率。
中文关键词:人脸验证  属性预测  深度学习  卷积神经网络
 
Application Research of Face Attributes in Face Verification Based on Deep Learning
Abstract:Face attributes,such as gender and age,are unique to feature faces.Aiming at the research of traditional face verification ,we proposed a depth model based on attribute prediction method.The method is based on the deep convolution neural network model to extract the facial feature representation,and the classifier is trained by the data of labeled attribute information to predict the attributes.And we use it in face verification.This method provides a method for analyzing face attributes from facial features extracted from depth model.Experiments show that this method can effectively improve the accuracy of face verification in practical applications.
keywords:face verification  face attributes  deep learning  convolutional neural network
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