基于稠密多变量标签的“连续”头部姿态估计方法

Gao-li SANG; Hu CHEN; Ge HUANG; Qi-jun ZHAO

doi:10.1631/FITEE.1500235

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基于稠密多变量标签的“连续”头部姿态估计方法

常规文章 | Updated：2022-05-19

- 基于稠密多变量标签的“连续”头部姿态估计方法
- Unseen head pose prediction using dense multivariate label distribution
- 信息与电子工程前沿（英文） 2016年17卷第6期页码：516-526
- Affiliations：
  
  1State Key Laboratory of Fundamental Science on Synthetic Vision,College of Computer Science, Sichuan University, Chengdu 610064, China
  2College of Mathematics and Information Engineering, Jiaxing University, Jiaxing 314001, China
- Author bio：
  
  E-mail:g.sang@foxmail.com
  E-mail:huchen@scu.edu.cn
  E-mail:26434368@qq.com
  E-mail:qjzhao@scu.edu.cn
- Funds：
  
  Project supported by the National Key Scientific Instrument and Equipment Development Project of China (No.2013YQ49087903) and the National Natural Science Foundation of China (No.61202160)
- DOI：10.1631/FITEE.1500235
  中图分类号： TP391.4
- 收稿：2015-07-23，
  
  录用：2015-2-16，
  
  纸质出版：2016-06
- Accepted：
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基于稠密多变量标签的“连续”头部姿态估计方法[J]. 信息与电子工程前沿（英文）, 2016,17(6):516-526.

Gao-li SANG, Hu CHEN, Ge HUANG, et al. Unseen head pose prediction using dense multivariate label distribution[J]. Frontiers of Information Technology & Electronic Engineering, 2016, 17(6): 516-526.
基于稠密多变量标签的“连续”头部姿态估计方法[J]. 信息与电子工程前沿（英文）, 2016,17(6):516-526. DOI： 10.1631/FITEE.1500235.

Gao-li SANG, Hu CHEN, Ge HUANG, et al. Unseen head pose prediction using dense multivariate label distribution[J]. Frontiers of Information Technology & Electronic Engineering, 2016, 17(6): 516-526. DOI： 10.1631/FITEE.1500235.

摘要

目的

精确的头部姿态估计对于人脸相关的应用，如人脸识别、视线估计、情感分析等具有重要意义。大多数现有的人脸姿态估计方法仅能对训练数据库包含姿态的情况进行估计。为实现对训练数据库不包含姿态的情况进行预测，有学者提出了基于回归的头部姿态估计方法。然而，这些基于回归的方法虽然可以预测连续的姿态，但是却很少有相关的系统性性能评估。

方法

针对训练数据库不包含姿态的估计问题，本文提出使用稠密多变量标签分布表示人脸姿态。通过给样本分配稠密化的多变量标签，可以实现对数据库不包含姿态的情况进行较为准确的估计。

结论

本文方法在Pointing’04数据库上的yaw和pitch方向分别取得了平均绝对误差4.01°和2.13°。此外，在CAL-PEAL，Multi-PIE等公开库上的实验表明，本文方法在训练数据库包含姿态上的预测性能也优于其他比较先进的方法。

Abstract

Accurate head poses are useful for many face-related tasks such as face recognition

gaze estimation

and emotion analysis. Most existing methods estimate head poses that are included in the training data (i.e.

previously seen head poses). To predict head poses that are not seen in the training data

some regression-based methods have been proposed. However

they focus on estimating continuous head pose angles

and thus do not systematically evaluate the performance on predicting unseen head poses. In this paper

we use a dense multivariate label distribution (MLD) to represent the pose angle of a face image. By incorporating both seen and unseen pose angles into MLD

the head pose predictor can estimate unseen head poses with an accuracy comparable to that of estimating seen head poses. On the Pointing’04 database

the mean absolute errors of results for yaw and pitch are 4.01° and 2.13°

respectively. In addition

experiments on the CAS-PEAL and CMU Multi-PIE databases show that the proposed dense MLD-based head pose estimation method can obtain the state-of-the-art performance when compared to some existing methods.

关键词

Keywords

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Multiclass classification based on a deep convolutional network for head pose estimation