摘要： 针对传统深度卷积神经网络模型复杂、识别速度慢的问题，提出一种基于多任务学习的人脸属性识别方法。通过轻量化残差模块构建基础网络，根据属性类之间的关联关系设计共享分支网络，以大幅减少网络参数和计算开销。以多任务学习的方式联合优化各分支网络与基础网络的参数，利用关联属性间的共同特征实现人脸属性识别。采用带权重的交叉熵作为损失函数监督训练网络模型，改善正负样本数不均衡问题。在公开数据集CelebA上的实验结果表明，该方法的识别错误率低至8.45%，空间开销仅2.7 MB，在CPU上每幅图预测时间低至15 ms，方便部署在资源有限的移动或便携式设备上，具有实际应用价值。
Abstract: To address the problem of complex model and slow recognition speed of traditional deep convolutional neural network,this paper proposes a face attributes recognition method based on multi-task learning.The underlying network is constructed by the lightweight residual modules.According to the correlations between attribute classes,the sharing branch networks are designed to largely reduce the network parameters and calculation costs.Then the parameters of the branch networks and underlying network are jointly optimized in the manner of multi-task learning.The shared features of correlated attributes are used to achieve better recognition effect.The weighted cross entropy is taken as the supervised training network model of the loss function,so as to improve the disequilibrium of positive and negative samples.Experimental results on the public dataset CelebA show that the recognition error rate of the proposed algorithm can be reduced to 8.45% and the space cost is only 2.7 MB.Besides,the prediction time of each image on CPU is reduced to 15 ms,which is suitable for the resource-limited portable devices and valuable in real life applications.
face attributes recognition,
lightweight residual module,
deep convolutional neural network,