Fully automatic facial feature point detection using Gabor feature based boosted classifiers
By Danijela Vukadinovic, Maja Pantic. IEEE International Conference on Systems, Man and Cybernetics, 2005
316 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/45f3bf505f1ce9cc600c867b1fb2aa5edd5feed8
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In Computer Vision and Pattern Recognition
- Luc Van Gool
- Xiaoou Tang
- Xiaogang Wang
- Dacheng Tao
- Chen Change Loy
- Ming-Hsuan Yang
- Andrea Vedaldi
- Trevor Darrell
Citations in the corpus
- Continuous Prediction of Spontaneous Affect from Multiple Cues and Modalities in Valence-Arousal Space (2011, methodology): “Prior to tracking, initialization of the facial feature points has been done using the method introduced in [61].”
- Dynamics of facial expression: recognition of facial actions and their temporal segments from face profile image sequences (2006, methodology): “We assume that they are initialized either manually or automatically in the first frame of the input face image sequence (e.g., using the method proposed in [25] and/or in [36]) and they are automatically tracked for the rest of the sequence by applying a particle filtering method.”
- Localizing Parts of Faces Using a Consensus of Exemplars (2013, background): “A few authors have released software implementations of their facial feature point detection method [10], [28], [33], and because of the utility of detected fiducial points, commercial products have become available by Betaface, face.”
- Fully Automatic Facial Action Unit Detection and Temporal Analysis (2006, methodology): “1) plus the irises and the center of the mouth in a face image, uses Gabor-featurebased boosted classifiers as proposed in [20].”
- Facial point detection using boosted regression and graph models (2010, methodology): “Our method is also more accurate than the current state of the art in facial point detecti on [3, 14, 23].”
- Face Alignment by Explicit Shape Regression (2012, methodology): “Figure 5 compares our method with previous methods [20, 5, 14, 19, 1].”
- Real-time facial feature detection using conditional regression forests (2012, methodology): “Vukadinovic and Pantic [31] train independent GentleBoost detectors for 20 facial points using Gabor filters' responses.”