为解决传统人脸属性分类训练效率低、模型参数量巨大等问题,本文提出了一种多网络学习框架。该框架包含两个子网络:人脸区域定位网络和属性分类网络,从而实现动态选择不同的人脸区域进行人脸属性预测。此外,本文提出了基于提示的模型压缩技术,在不明显降低准确率的前提下大大压缩网络参数。最后,通过仿真在CelebA人脸属性分类数据集上对所提方法进行性能分析,所提出网络模型平均分类准确率可达到90.89%,网络参数量仅为0.27M。

In order to solve the shortcomings of traditional face attribute recognition, such as low training efficiency and huge model parameters, a multi network learning framework is proposed.The framework consists of two sub networks:face region location network and attribute classification network, so as to dynamically select different face regions for face attribute prediction In addition, this paper proposes a hint based model compression technique, which greatly compresses the network parameters without significantly reducing the accuracy.Finally, the performance of the proposed method is analyzed by simulation on celeba face attribute classification data set.The average classification accuracy of the proposed network model can reach 90.89%, and the network parameter is only 0.27M.