Real time head pose estimation with random regression forests

By Gabriele Fanelli, Juergen Gall, Luc Van Gool. 2011

504 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

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Citations in the corpus

Alternating Regression Forests for Object Detection and Pose Estimation (2013, background): “Another task is human head pose estimation [7] from single depth images, where a Random Regression Forest is trained to estimate the position of the head (e.”
Decision Forests for Classification, Regression, Density Estimation, Manifold Learning and Semi-Supervised Learning (2011, background): “Although (non-probabilistic) regression forests were described in [11] they have only recently started to be used in computer vision and medical image analysis [24, 29, 37, 49, 59].”
Rendering of Eyes for Eye-Shape Registration and Gaze Estimation (2015, background): “Since faces exhibit large color and texture variability, some approaches side-stepped this by relying on depth images [17, 19], and synthesizing depth images of the head using existing datasets or a deformable headshape model.”
Real-time facial feature detection using conditional regression forests (2012, methodology): “Therefore, we simplify the distribution over the offset by a multivariate Gaussian as in [8, 12]:”
Results and Analysis of the ChaLearn Gesture Challenge 2012 (2012, background): “– The third place system was by a team from Switzerland that performed real-time head pose estimation [6], for the purpose of improving the realism of sound simulation.”
A Comprehensive Analysis of Deep Regression (2018, unknown): “In computer vision, regression techniques span a large ensemble of applicative scenarios such as: head-pose estimation [1], [2], facial landmark detection [3], [4], human pose estimation [5], [6], age estimation [7], [8], or image registration [9], [10].”
A Survey on Human Motion Analysis from Depth Data (2013, methodology): “Head pose estimation with regression forests [66].”
OpenFace: An open source facial behavior analysis toolkit (2016, methodology): “Furthermore, all of the approaches we compared against provide only binaries with pre-trained models and not the full training and testing code (except for EyeTab [63] and regression forests [21]).”
Real Time Head Pose Estimation from Consumer Depth Cameras (2011, methodology): “The method is inspired by the recent work of [10] that uses random regression forests [9] to estimate the 3D head pose in real time from high quality depth data.”
Random Forests for Real Time 3D Face Analysis (2012, methodology): “Inspired by the works (Gall and Lempitsky 2009; Criminisi et al. 2010), we have shown that regression forests can be used for real time head pose estimation from depth data (Fanelli et al. 2011a, 2011b).”
Deep Reconstruction Models for Image Set Classification (2015, background): “Index Terms—Image set classification, deep learning, auto-encoders, video based face recognition, object recognition Ç”
Decision Forests: A Unified Framework for Classification, Regression, Density Estimation, Manifold Learning and Semi-Supervised Learning (2012, background): “Recent years have seen an explosion of forest-based techniques in the machine learning, vision and medical imaging literature [9, 15, 25, 31, 35, 37, 58, 59, 65, 67, 68, 69, 74, 79, 89, 92, 97, 110].”
Deep Label Distribution Learning With Label Ambiguity (2016, background): “Therefore, exploiting deep learning methods with limited samples and ambiguous labels has become an attractive yet challenging topic.”
Latent-Class Hough Forests for 3D Object Detection and Pose Estimation (2014, methodology): “Thus, doing a naive holistic patch comparison, or the two-dimenson/ two-pixel tests (as used in [29,8,33]) can lead to test patches taking the incorrect route at split functions.”
OpenFace 2.0: Facial Behavior Analysis Toolkit (2018, methodology): “There also exists a random forest based framework that allows for head pose estimation using depth data [24].”
Dense Non-rigid Shape Correspondence Using Random Forests (2014, result): “This result contrasts the common setting in which random forests are trained with copious amounts of data [22, 6], making the approach rather practical when only limited training data is available.”
3D Head Pose Estimation for TV Setups (2013, background): “More recently, with the arrival of low cost depth sensor, more accurate solutions have emerged [6][8].”