Robust Head-Pose Estimation Based on Partially-Latent Mixture of Linear Regressions

2016-03-31 · Vincent Drouard, Radu Horaud, Antoine Deleforge, Silèye Ba, Georgios Evangelidis

Head-pose estimation has many applications, such as social event analysis, human-robot and human-computer interaction, driving assistance, and so forth. Head-pose estimation is challenging because it must cope with changing illumination conditions, variabilities in face orientation and in appearance, partial occlusions of facial landmarks, as well as bounding-box-to-face alignment errors. We propose tu use a mixture of linear regressions with partially-latent output. This regression method learns to map high-dimensional feature vectors (extracted from bounding boxes of faces) onto the joint space of head-pose angles and bounding-box shifts, such that they are robustly predicted in the presence of unobservable phenomena. We describe in detail the mapping method that combines the merits of unsupervised manifold learning techniques and of mixtures of regressions. We validate our method with three publicly available datasets and we thoroughly benchmark four variants of the proposed algorithm with several state-of-the-art head-pose estimation methods.

📄 PDF Abstract BibTeX arXiv:1603.09732

Code (0)

등록된 구현이 없습니다.

Tasks

Face Alignment Head Pose Estimation Pose Estimation

Robust Mixture Learning when Outliers Overwhelm Small Groups

2024-07-22 · Daniil Dmitriev, Rares-Darius Buhai, Stefan Tiegel, Alexander Wolters 외

We study the problem of estimating the means of well-separated mixtures when an adversary may add arbitrary outliers. While strong guarantees are available when the outlier fraction is significantly smaller than the mini…

Forward $χ^2$ Divergence Based Variational Importance Sampling

2023-11-04 · Chengrui Li, Yule Wang, Weihan Li, Anqi Wu

Maximizing the log-likelihood is a crucial aspect of learning latent variable models, and variational inference (VI) stands as the commonly adopted method. However, VI can encounter challenges in achieving a high log-lik…

parameter estimation Variational Inference

Causal Inference and Mechanism Clustering of A Mixture of Additive Noise Models

2018-09-23 · NeurIPS 2018 12 · Shoubo Hu, Zhitang Chen, Vahid Partovi Nia, Laiwan Chan 외

The inference of the causal relationship between a pair of observed variables is a fundamental problem in science, and most existing approaches are based on one single causal model. In practice, however, observations are…

Causal Inference Clustering

Learning Mixture Density via Natural Gradient Expectation Maximization

2026-02-11 · Yutao Chen, Jasmine Bayrooti, Steven Morad arxiv

Mixture density networks are neural networks that produce Gaussian mixtures to represent continuous multimodal conditional densities. Standard training procedures involve maximum likelihood estimation using the negative …

SkyNet: Belief-Aware Planning for Partially-Observable Stochastic Games

2026-03-29 · Adam Haile arxiv

In 2019, Google DeepMind released MuZero, a model-based reinforcement learning method that achieves strong results in perfect-information games by combining learned dynamics models with Monte Carlo Tree Search (MCTS). Ho…

Reinforcement Learning