一种用于人脸表情识别的分层金字塔神经网络结构
A Sub-Layered Hierarchical Pyramidal Neural Architecture for Facial Expression Recognition
摘要：
在计算资源和标记数据有限的领域，例如机器人领域，具有数百万权重的深度网络可能不是最佳解决方案。在本文中，我们介绍了一个金字塔架构的连接方案，以增加其学习功能的能力。对未见过的人的面部表情识别的实验表明，我们的方法是一个潜在的候选人的应用资源有限，由于良好的泛化性能和低计算成本。我们表明，我们的方法在这项任务中推广了卷积架构，但使用了更少的可训练参数，并且对低分辨率人脸更鲁棒。
英文摘要：
In domains where computational resources and labeled data are limited, such as in robotics, deep networks with millions of weights might not be the optimal solution. In this paper, we introduce a connectivity scheme for pyramidal architectures to increase their capacity for learning features. Experiments on facial expression recognition of unseen people demonstrate that our approach is a potential candidate for applications with restricted resources, due to good generalization performance and low computational cost. We show that our approach generalizes as well as convolutional architectures in this task but uses fewer trainable parameters and is more robust for low-resolution faces.
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