scholarly journals Deep convolutional neural network-based Bernoulli heatmap for head pose estimation

2021 ◽  
Vol 436 ◽  
pp. 198-209
Author(s):  
Zhongxu Hu ◽  
Yang Xing ◽  
Chen Lv ◽  
Peng Hang ◽  
Jie Liu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Pose Estimation ◽  
Deep Convolutional Neural Network ◽  
Head Pose Estimation ◽  
Head Pose

Head pose estimation with soft labels using regularized convolutional neural network

2019 ◽  
Vol 337 ◽  
pp. 339-353 ◽  
Author(s):  
Luhui Xu ◽  
Jingying Chen ◽  
Yanling Gan
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Pose Estimation ◽  
Head Pose Estimation ◽  
Head Pose

scholarly journals Head Pose Estimation Based on Robust Convolutional Neural Network

2016 ◽  
Vol 16 (6) ◽  
pp. 133-145 ◽  
Author(s):  
Jiao Bao ◽  
Mao Ye
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Pose Estimation ◽  
Network Architecture ◽  
Facial Feature ◽  
Head Pose Estimation ◽  
Head Pose ◽  
Whole Process ◽  
Benchmark Datasets ◽  
Better Than

Abstract Head pose estimation plays an important role in face recognition. However, it faces vast challenges on account of the initialization, facial feature points’ location accuracy and so on. Inspired by the observation that head pose angles change smoothly and continuously, we present a method based on a robust convolutional neural network for head pose estimation. The proposed network architecture consists of three levels and each level has three convolutional neural networks. The first level is a global one; it predicts the head pose quickly as a preliminary estimation. The following two levels are local ones; they refine the estimation achieved from the previous level step by step. Higher and higher resolution image with different input regions are taken as input in our network. At last, a multi-level regression is employed to combine the estimations from each level. The whole process is conducted in a cascade way to improve the head pose estimation performance directly with three angles together. We perform large experiments on nine challenging benchmark datasets. The experimental results demonstrate that our method performs better than the compared methods.

Head Pose Estimation Based on Multi-Scale Convolutional Neural Network

2019 ◽  
Vol 56 (13) ◽  
pp. 131003
Author(s):  
梁令羽 Lingyu Liang ◽  
张天天 Tiantian Zhang ◽  
何为 Wei He
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Pose Estimation ◽  
Head Pose Estimation ◽  
Head Pose ◽  
Multi Scale
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