Face Alignment Based on Statistical Models Using SIFT Descriptors
@article{Li2009FaceAB, title={Face Alignment Based on Statistical Models Using SIFT Descriptors}, author={Zisheng Li and Jun-ichi Imai and Masahide Kaneko}, journal={IEICE Trans. Fundam. Electron. Commun. Comput. Sci.}, year={2009}, volume={92-A}, pages={3336-3343}, url={https://api.semanticscholar.org/CorpusID:33839992} }
An improved ASM framework, GentleBoost based SIFT-ASM is proposed, which significantly outperforms the original ASM in aligning and localizing facial features.
Topics
Active Shape Models (opens in a new tab)Scale Invariant Feature Transform (opens in a new tab)Face Alignment (opens in a new tab)Gaussian Distribution (opens in a new tab)Landmarks (opens in a new tab)Gray-level Profile (opens in a new tab)Localize (opens in a new tab)SIFT Descriptor (opens in a new tab)
2 Citations
Face Recognition with Occlusion using a wireframe model and Support Vector Machine
- 2017
Computer Science
The wireframe model presents a robust coordinate system that even in complex cases, as data loss, is able to obtain a fairly good recognition rate.
9 References
Face alignment using statistical models and wavelet features
- 2003
Computer Science
A method in which Gabor wavelet features are used for modeling local image structure, in which the ability of W-ASM to accurately align and locate facial features is demonstrated.
Shape parameter optimization for Adaboosted active shape model
- 2005
Computer Science
An algorithm of modeling local appearances, called AdaBoosted ASM, and a shape parameter optimization method are proposed, which improves robustness of landmark displacement greatly, and solves the inadequacy problem of ASM on shape constraint effectively.
Distinctive Image Features from Scale-Invariant Keypoints
- 2004
Computer Science
This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene and can robustly identify objects among clutter and occlusion while achieving near real-time performance.
Active Shape Models-Their Training and Application
- 1995
Computer Science
This work describes a method for building models by learning patterns of variability from a training set of correctly annotated images that can be used for image search in an iterative refinement algorithm analogous to that employed by Active Contour Models (Snakes).
Robust Real-Time Face Detection
- 2001
Computer Science
A new image representation called the “Integral Image” is introduced which allows the features used by the detector to be computed very quickly and a method for combining classifiers in a “cascade” which allows background regions of the image to be quickly discarded while spending more computation on promising face-like regions.
A performance evaluation of local descriptors
- 2003
Computer Science
It is observed that the ranking of the descriptors does not depend on the point detector and that SIFT descriptors perform best and steerable filters can be considered a good choice given the low dimensionality.
Snakes: Active contour models
- 2004
Computer Science
This work uses snakes for interactive interpretation, in which user-imposed constraint forces guide the snake near features of interest, and uses scale-space continuation to enlarge the capture region surrounding a feature.
Active Appearance Models
- 1998
Computer Science
A new method of matching statistical models of appearance to images by learning the relationship between perturbations in the model parameters and the induced image errors is described.
Special Invited Paper-Additive logistic regression: A statistical view of boosting
- 2000
Computer Science, Mathematics
This work shows that this seemingly mysterious phenomenon of boosting can be understood in terms of well-known statistical principles, namely additive modeling and maximum likelihood, and develops more direct approximations and shows that they exhibit nearly identical results to boosting.