基于深度代价敏感CNN的年龄估计算法

针对年龄估计中样本数量不平衡及不同类间发生误分类时付出代价不同的问题,将代价敏感性嵌入深度学习框架中,提出基于深度代价敏感CNN的年龄估计算法.首先为每个年龄类别分别建立损失函数,解决训练样本的不平衡问题.然后,定义代价向量以反映不同类之间发生误分类而付出的代价差异性,构造逆交叉熵误差函数.最后,融合上述方法,为卷积神经网络(CNN)构造一个损失函数,使CNN在训练阶段学习针对年龄估计的鲁棒人脸表征.在不同种族的年龄估计标准图像集上的实验验证文中算法的有效性.

Age Estimation Algorithm Based on Deep Cost Sensitive CNN

Aiming at the problems of the imbalance of sample size in age estimation and the cost of misclassification between different classes,the cost sensitivity is embedded into the deep learning framework,and an age estimation algorithm based on deep cost sensitive convolutional neural networks(CNN)is proposed.Firstly,a loss function is established for each age category to solve the imbalance problem of the training samples.Then,a cost vector is defined to reflect the cost difference caused by misclassification between different classes,and an inverse cross entropy error function is constructed.Finally,the above methods are merged to derive a loss function for CNN to learn the robust face representation for age estimation during the training process.Experiments on different age estimation standard image sets verify the effectiveness of the proposed algorithm.