Head Pose Estimation Based on Random Forests for Multiclass Classification
Head Pose Estimation Based on Random Forests for Multiclass Classification
复制标题
DOI：
10.1109/icpr.2010.234
复制
发表时间：
2010-08
期刊：
影响因子：
--
通讯作者：
Chen Huang;Xiaoqing Ding;Chi Fang
中科院分区：
文献类型：
--
作者：
Chen Huang;Xiaoqing Ding;Chi Fang
由于身份变化、光照变化、噪声等原因，头部姿势估计对于计算机视觉系统来说仍然是一个独特的挑战。以前的统计方法，如 PCA、线性判别分析 (LDA) 和机器学习方法，包括 SVM 和 Adaboost，不能很好地实现准确性和鲁棒性。在本文中，我们建议使用基于 Gabor 特征的随机森林作为分类技术，因为它们自然地处理此类多类分类问题并且准确且快速。随机性的两个来源，随机输入和随机特征，使随机森林变得强大并且能够处理大的特征空间。此外，我们采用LDA作为节点测试，以提高森林中各个树的判别能力，每个节点生成恒定和变化数量的子节点。在两个公共数据库上进行的实验表明，所提出的算法在准确性和计算效率方面均优于其他方法。
Head pose estimation remains a unique challenge for computer vision system due to identity variation, illumination changes, noise, etc. Previous statistical approaches like PCA, linear discriminative analysis (LDA) and machine learning methods, including SVM and Adaboost, cannot achieve both accuracy and robustness that well. In this paper, we propose to use Gabor feature based random forests as the classification technique since they naturally handle such multi-class classification problem and are accurate and fast. The two sources of randomness, random inputs and random features, make random forests robust and able to deal with large feature spaces. Besides, we implement LDA as the node test to improve the discriminative power of individual trees in the forest, with each node generating both constant and variant number of children nodes. Experiments are carried out on two public databases to show the proposed algorithm outperforms other approaches in both accuracy and computational efficiency.