scholarly journals Robust Head Pose Estimation Using Extreme Gradient Boosting Machine on Stacked Autoencoders Neural Network

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 3687-3694 ◽  
Author(s):  
Minh Thanh Vo ◽  
Trang Nguyen ◽  
Tuong Le
Keyword(s):  
Neural Network ◽  
Pose Estimation ◽  
Head Pose Estimation ◽  
Gradient Boosting ◽  
Head Pose ◽  
Stacked Autoencoders ◽  
Gradient Boosting Machine ◽  
Extreme Gradient Boosting

Real-time head pose estimation using multi-task deep neural network

2018 ◽  
Vol 103 ◽  
pp. 1-12 ◽  
Author(s):  
Byungtae Ahn ◽  
Dong-Geol Choi ◽  
Jaesik Park ◽  
In So Kweon
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Pose Estimation ◽  
Deep Neural Network ◽  
Head Pose Estimation ◽  
Head Pose

scholarly journals Gradient boosting regression for faster Partitioned Iterated Function Systems‐based head pose estimation

2021 ◽  
Author(s):  
Paola Barra ◽  
Riccardo Distasi ◽  
Chiara Pero ◽  
Stefano Ricciardi ◽  
Maurizio Tucci
Keyword(s):  
Pose Estimation ◽  
Iterated Function Systems ◽  
Head Pose Estimation ◽  
Gradient Boosting ◽  
Head Pose ◽  
Iterated Function

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.

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