Facial point detection using boosted regression and graph models

By Michel François Valstar, Brais Martínez, Xavier Binefa, Maja Pantic. 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010

351 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/7e6d4876d4b25b0aa31af2c3437fb3f640e16694

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In Computer Vision and Pattern Recognition

Citations in the corpus

Learning Deep Representation for Face Alignment with Auxiliary Attributes (2014, methodology): “This method shows superior accuracy compared to previous methods [9], [14] and existing commercial systems.”
Localizing Parts of Faces Using a Consensus of Exemplars (2013, background): “While a number of approaches balance local feature detector responses on the image with prior global information about the feature configurations [6], [7], [13], [21], [25], [26], optimizing the resulting objective function remains a challenge.”
Face Alignment at 3000 FPS via Regressing Local Binary Features (2014, methodology): “Our work belongs to the shape regression approach [5, 11, 12, 29, 4, 32, 3] category.”
A Survey on Human Motion Analysis from Depth Data (2013, methodology): “In recent years, there has been a shift towards methods based on independent local feature detectors [74, 75, 76, 77].”
Face Alignment by Explicit Shape Regression (2012, methodology): “Our Method Vukadinovic and Pantic[20] Cristinacce and Cootes[5] Milborrow and Nicolls[14] Valstar et al.[19] Kumar et al.[1] Figure 5.”
Facial Landmark Detection by Deep Multi-task Learning (2014, background): “[24] predict landmark location from local image patch with support vector regression.”
Coarse-to-Fine Auto-Encoder Networks (CFAN) for Real-Time Face Alignment (2014, methodology): “Generally speaking, the existing approaches can be categorized into holistic feature based methods [7,21,14,34,19,6] and local feature based methods [8,10,15,23,9,25,35,32,31,2,28,11].”
Face Alignment in Full Pose Range: A 3D Total Solution (2015, background): “[34] locate landmark positions by mapping the landmark related local patches with support vector regression.”
Automatic Analysis of Facial Affect: A Survey of Registration, Representation, and Recognition (2015, methodology): “Alternative facial feature detectors are also used [152], [162].”
Unsupervised Learning of Object Landmarks by Factorized Spatial Embeddings (2017, background): “Other approaches directly regress the landmark coordinates [59, 14, 10, 52].”
Random Forests for Real Time 3D Face Analysis (2012, methodology): “In recent years, there has been a shift towards methods based on independent local feature detectors (Valstar et al. 2010; Amberg and Vetter 2011; Belhumeur et al. 2011).”
3D Face Pose Tracking using Low Quality Depth Cameras (2013, methodology): “point detection algorithm (Valstar et al., 2010) on the detected face to provide L=22 landmark points lr such as eye corners, the nose tip, etc.”
Real-time facial feature detection using conditional regression forests (2012, background): “Due to its relevance for many applications like human computer interaction or face analysis, facial feature point detection is a very active area in computer vision [1, 3, 24, 25, 29].”
Unsupervised Discovery of Object Landmarks as Structural Representations (2018, methodology): “For human faces, there are active appearance models [10, 38, 11], template-based methods [42, 83], regression-based methods [61, 12, 7, 48], and more recent methods based on deep neural networks [55, 77, 81, 82, 75, 70, 68, 33, 71].”
Large-Pose Face Alignment via CNN-Based Dense 3D Model Fitting (2016, background): “Recently, face alignment works are very popular in top vision venues, as demonstrated by the progress in Constrained Local Model based approaches [5, 22], AAM-based approaches [15, 13, 14] and regressionbased approaches [27, 4, 33].”
Deep Convolutional Network Cascade for Facial Point Detection (2013, methodology): “Facial keypoint detection is critical for face recognition and analysis, and has been studied extensively in recent years [3] [4] [5] [8] [9] [11] [20] [21] [23] [25] [26] [27] [28].”
Robust Face Landmark Estimation under Occlusion (2013, background): “Another option is to tackle shape estimation as a regression problem, learning regressors that directly predict the object shape or the location of its parts, starting from a raw estimate of its position [39, 14, 16, 12, 7, 42, 25, 6].”