基于多级子网络和排序性Dropout机制的人脸属性识别

如何提高自然环境下或非受限环境下人脸属性识别的准确率是应用人脸属性的一个重要问题。在日常生活中，人脸姿势和光照等不可控制的因素对识别人脸属性产生了较大影响，如何在上述因素影响下提高识别的精度是我们研究人脸属性识别的关键问题。目前卷积神经网络（Convolutional neural network，CNN）在图像分类中已经取得显著性成果，本文通过采用多级子网络和排序性Dropout机制算法重新构建一个网络结构，该结构对处理人脸姿势变化等具有较强的鲁棒性，在CelebA数据集和LFWA数据集中取得较好的效果，且大大降低了网络体积。

Face Attributes Recognition by Multi-level Sub-network and Ranked Dropout Mechanism

How to improve the accuracy of face attributes recognition in natural environment or unrestricted environment is an important question in applying face attributes. In daily life, the uncontrollable factors, such as face postures and light, have a great influence on the recognition of human face attributes. How to improve the accuracy under the influence of the above factors is a key problem in the study of face attribute recognition. Given the success of convolutional neural network (CNN) in image classification, a new network structure is built by using multi-level sub-network and ranked Dropout mechanism algorithm. The structure has strong robustness to deal with face changes, thus achieving better results in the CelebA dataset and LFWA dataset, and reducing the network size significantly as well.