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首页> 外文会议>IEEE International Conference on Information Technology, Big Data and Artificial Intelligence >Convolutional Neural Networks for Mobile Face Recognition with Hierarchical Feature integration
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Convolutional Neural Networks for Mobile Face Recognition with Hierarchical Feature integration

机译：具有分层特征集成的移动人脸识别的卷积神经网络

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摘要

Significant advances in face recognition tasks have been made with the development of convolutional neural networks. With large scale convolutional neural networks we are now able to achieve 99% accuracy on many public face test sets. However, the face recognition networks commonly used for mobile are limited by their narrow network width which makes it difficult to learn better distributed features in face recognition feature embedding learning. In this work, based on MobileFaceNets we propose an attention-guided hierarchical feature fusion model. Compared with the traditional cascading convolutional neural network classification model, our approach can explicitly consider both global and local features. And our approach requires only a small increase in computation to obtain better results than directly increasing the feature dimension.
机译：已经通过卷积神经网络的发展进行了面部识别任务的显着进展。对于大规模的卷积神经网络，我们现在能够在许多公共面部测试集上达到99％的准确性。然而，常用于移动的人脸识别网络受到窄网络宽度的限制，这使得难以学习面部识别特征嵌入学习的更好的分布特征。在这项工作中，基于MobileFaceNets，我们提出了一种注意引导的等级特征融合模型。与传统的级联卷积神经网络分类模型相比，我们的方法可以明确考虑全局和本地功能。我们的方法只需要一个小的计算增加，以获得比直接增加特征维度的更好的结果。

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