基于深度模型的属性学习在人脸验证中的应用研究

Application Research of Face Attributes in Face Verification Based on Deep Learning

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。

Face attributes, such as gender and age,are unique to feature faces.Aiming at the research of traditional face verifi- cation ,we proposed a depth model based on attribute prediction method.The method is based on the deep convolution neural net- work 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 fi＇om facial fea- tures extracted from depth model.Experiments show that this method can effectively improve the accuracy of face verification in practical applications.