| 引入全局约束的精简人脸关键点检测网络 |
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| 引用本文： | 张伟,钱沄涛.引入全局约束的精简人脸关键点检测网络[J].信号处理,2019,35(3):507-515. |
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| 作者姓名： | 张伟 钱沄涛 |
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| 作者单位： | 浙江大学计算机学院 |
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| 基金项目： | 国家重点研发计划（2018YFB0505000） |
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| 摘 要： | 人脸关键点检测是计算机视觉中的典型问题之一,对于人脸三维重建、表情识别、头部姿态估计、人脸跟踪等有重要影响。目前基于深度神经网络的模型在人脸关键点检测性能表现最为突出,已被广泛采用。但是现有关键点检测深度神经网络结构设计越来越复杂,对于训练和测试需要的计算和存储资源要求越来越高。本文提出一种新的精简的关键点检测网络结构以代替现有的网络结构。相对其他网络结构,精简网络只包含一个特征提取模块,以及由几层反卷积层组成的上采样模块。此外我们在网络结构中加入对人脸所有关键点的全局约束,以减少预测离群点的产生。实验表明引入全局约束的精简网络结构在300-W数据集上取得的检测性能超出了目前典型深度神经网络检测模型。
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| 关 键 词： | 深度学习 卷积神经网络 全局约束 人脸关键点检测 |
| 收稿时间： | 2019-01-02 |
Streamlined face landmark detection network with global constraint |
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| Affiliation: | College of Computer Science, Zhejiang University |
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| Abstract: | Face landmark detection is one of the typical problems in computer vision, which has important influence on face 3D reconstruction, expression recognition, head pose estimation, face tracking and so on. At present, the model based on deep neural network is most popular in this field. However, as the existing key point detection deep neural network structure design getting more complex, it requires much more the computing and storage resources . In this .paper, We propose a new streamlined landmark detection network structure to replace the existing network structure. Compared with other network structures, a simple network only consists of one feature extraction module and an upsampling module made up of several deconvolution layers. In addition, we add global constraints on all key points of the face in the network structure to reduce the generation of predicted outliers. Experiments show that the detection performance of this network structure with global constraints on the 300-W data has better performance than the current typical deep neural network detection model. |
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| Keywords: | deep learning convolutional neural network global constraints face landmark detection |
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