scholarly journals Directional Correlation Filter Bank for Robust Head Pose Estimation and Face Recognition

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Si Chen ◽  
Dong Yan ◽  
Yan Yan
Keyword(s):  
Face Recognition ◽  
Pose Estimation ◽  
Filter Bank ◽  
Facial Feature ◽  
Correlation Filter ◽  
Head Pose Estimation ◽  
Superior Performance ◽  
Head Pose ◽  
Large Head ◽  
Wide Range

During the past few decades, face recognition has been an active research area in pattern recognition and computer vision due to its wide range of applications. However, one of the most challenging problems encountered by face recognition is the difficulty of handling large head pose variations. Therefore, the efficient and effective head pose estimation is a critical step of face recognition. In this paper, a novel feature extraction framework, called Directional Correlation Filter Bank (DCFB), is presented for head pose estimation. Specifically, in the proposed framework, the 1-Dimensional Optimal Tradeoff Filters (1D-OTF) corresponding to different head poses are simultaneously and jointly designed in the low-dimensional linear subspace. Different from the traditional methods that heavily rely on the precise localization of the key facial feature points, our proposed framework exploits the frequency domain of the face images, which effectively captures the high-order statistics of faces. As a result, the obtained features are compact and discriminative. Experimental results on public face databases with large head pose variations show the superior performance obtained by the proposed framework on the tasks of both head pose estimation and face recognition.

scholarly journals FDN: Feature Decoupling Network for Head Pose Estimation

2020 ◽  
Vol 34 (07) ◽  
pp. 12789-12796
Author(s):  
Hao Zhang ◽  
Mengmeng Wang ◽  
Yong Liu ◽  
Yi Yuan
Keyword(s):  
Pose Estimation ◽  
Network Architecture ◽  
Latent Variable ◽  
Spatial Information ◽  
Head Pose Estimation ◽  
Depth Information ◽  
Head Pose ◽  
Large Head ◽  
In The Wild ◽  
Rgb Image

Head pose estimation from RGB images without depth information is a challenging task due to the loss of spatial information as well as large head pose variations in the wild. The performance of existing landmark-free methods remains unsatisfactory as the quality of estimated pose is inferior. In this paper, we propose a novel three-branch network architecture, termed as Feature Decoupling Network (FDN), a more powerful architecture for landmark-free head pose estimation from a single RGB image. In FDN, we first propose a feature decoupling (FD) module to explicitly learn the discriminative features for each pose angle by adaptively recalibrating its channel-wise responses. Besides, we introduce a cross-category center (CCC) loss to constrain the distribution of the latent variable subspaces and thus we can obtain more compact and distinct subspaces. Extensive experiments on both in-the-wild and controlled environment datasets demonstrate that the proposed method outperforms other state-of-the-art methods based on a single RGB image and behaves on par with approaches based on multimodal input resources.

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.

Learning a Deep Regression Forest for Head Pose Estimation from a Single Depth Image

2020 ◽  
pp. 2150139
Author(s):  
Xiangtian Ma ◽  
Nan Sang ◽  
Shihua Xiao ◽  
Xupeng Wang
Keyword(s):  
Pose Estimation ◽  
Feature Space ◽  
Facial Feature ◽  
Head Pose Estimation ◽  
Depth Image ◽  
Expression Recognition ◽  
Feature Points ◽  
Head Pose ◽  
Physical Systems ◽  
Driving Assistance

Robust head pose estimation significantly improves the performance of applications related to face analysis in Cyber-Physical Systems (CPS) such as driving assistance and expression recognition. However, there exist two main challenges in this issue, i.e., the large pose variations and the property of inhomogeneous facial feature space. Head pose in large variations makes the distinguished facial features, such as nose or lips, invisible, especially in extreme cases. Additionally, features extracted from a head do not change in a stationary manner with respect to the head pose, which results in an inhomogeneous feature space. To deal with the above problems, we propose an end-to-end framework to estimate the head pose from a single depth image. To be specific, the PointNet network is adopted to automatically select distinguished facial feature points from visible surface of a head and to extract discriminative features. The Deep Regression Forest is utilized to handle the nonstationary property of the facial feature space and to learn the head pose distributions. Experimental results show that our proposed method achieves the state-of-the-art performance on the Biwi Kinect Head Pose Dataset, the Pandora Dataset and the ICT-3DHP Dataset.

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