一种基于融合深度卷积神经网络与度量学习的人脸识别方法

A face recognition method based on fusion of deep CNN and metric learning

现有的卷积神经网络方法大多以增大类间距离为学习目标,而忽略类内距离的减小,这对于人脸识别来说,将导致一些非限制条件下(如姿态、光照等)的人脸无法被准确识别,为了解决此问题,提出一种基于融合度量学习算法和深度卷积神经网络的人脸识别方法。首先,提出一种基于多Inception结构的人脸特征提取网络,使用较少参数来提取特征;其次,提出一种联合损失的度量学习方法,将分类损失和中心损失进行加权联合;最后,将深度卷积神经网络和度量学习方法进行融合,在网络训练时,达到增大类间距离同时减小类内距离的学习目标。实验结果表明,该方法能提取出更具区分性的人脸特征,与分类损失方法及融合了其他度量学习方式的方法相比,提升了非限制条件下的人脸识别准确率。

The current convolutional neural network(CNN)methods mostly take the increase of inter-class distance as the learning objective,but ignore the decrease of intra-class distance,which makes that the human face can′t be recognize accurately under some unrestricted conditions(such as posture and illumination).In order to eliminate the above problem,a face recogni-tion method based on deep CNN and metric learning method is proposed.A face feature extraction network based on multi-Incep-tion structure is presented to extract the feature with less parameters.A metric learning method based on joint loss is presented to perform the weighting joint for the softmax loss and center loss.The deep CNN and metric learning method are fused to reach the learning objective of inter-class distance increase and intra-class distance decrease.The experimental results indicate that the proposed method can extract the more discriminative facial features,and improve the more face recognition accuracy under unrestricted conditions than the Softmax loss method and methods fusing other metric learning modes.