基于集成卷积神经网络的人脸年龄分类算法研究

人脸年龄估计由于在人机交互和安全控制等领域有潜在应用,因此得到了广泛关注。文中主要进行人脸年龄分组的研究,针对人脸年龄分类问题提出了一种基于集成卷积神经网络的年龄分类算法。首先,训练两个以人脸图像为输入的卷积神经网络,当用卷积神经网络直接提取人脸图像的特征时,主要对 深度的全局特征 进行提取。为了补充人脸图像的局部特征,尤其是纹理信息,将提取的LBP(Local Binary Pattern)特征作为另一个网络的输入。最后,为了结合人脸的全局特征和局部特征,将这3个网络进行集成。该算法在广泛使用的年龄分类数据集Group上取得了不错的效果。

Face Age Classification Method Based on Ensemble Learning of Convolutional Neural Networks

Face age estimation has attracted much attention due to its potential applications in the areas of human-computer interaction and safety control.This paper focused on face age classification task,and proposed an age classification algorithm based on ensemble convolutional neural network for face age classification.Firstly,two convolutional neural networks which make face images as input are trained,and the deeply global features are extracted mainly by using convolutional neural network.In order to further supply the local features of face images,especially texture information,the extracted LBP feature will be taken as input for another network.Finally,in order to combine the global features and the local features of the face images,three networks are integrated to generate good results in the widely used age estimation dataset.