Table 1 An overview of the texture-based face landmarking algorithms
Work | Highlights of the method | Domain knowledge used | Landmark types |
|---|---|---|---|
Yuille et al.[72], 1989 | Using image saliencies of the face components, geometrical templates are developed consisting of arcs and circles. Eye template consists of a circle for iris, two parabola sections for eye contours, two center points for the white sclera. | Descriptive information of the eye andmouth geometries. | Eye, iris and mouth contours.
|
Pentland et al.[18], 1994 | Extension of the eigenface approach to eigenmouth, eigeneye and eigennose. Multiple eigenspaces mitigate variations due to pose. Face-ness, mouth-ness etc. are assessed based on the concept of distance from corresponding (eye, mouth, nose etc.) eigenspace. | None. | Mouth, nose and individualeye components.
|
Vukadinovic &Pantic [44], 2005 | GentleBoost templates built from both gray level intensities and Gabor wavelet features. A sliding search is run with templates over twenty face regions. | Face initially divided into search regions on the basis of IOD vis-Ã -vis the detected eyes. In addition horizontal and vertical projection histograms and symmetry of the frontal face are used. | 20 landmarks.
|
Arca et al. [41],2006 | Face is detected with skin features, and eyes are located using SVM. Facial components are extracted using parametric curves specific to each component as in [72], and facial landmarks are traced on these curves. | Various facial component heuristics such as the vertically alignment of the eyes, the mouth is centered with respect to the eye positions etc. | 16 landmarks
|
Zhang & Ruan[73], 2006 | Rectangular eyes, mouth and nose templates resulting from averaging several instances used for detection. Geometrical templates consisting of arcs and circles are fitted to components for detailed modeling. | Eye and mouth geometry. | Eye, iris and mouth contours.
|
Templates based on 50% of block DCT features (block size 0.4×IOD) scan the image and SVM score map is obtained. Initial combinatorial search decides for 7 fiducial landmark, and the rest of the landmarks are predicted and locally tested with their DCT features. | Landmark distances and angles are learned, modeled as Gaussians and the information embedded in a graph. | 17 landmarks.
| |
Ding & Martinez[68], 2010 | Face components are found via Subclass Determinant Analysis, where multiple models for the target component, eyes and mouth are developed; the context is the subspace representation of the regions surrounding the components. | Estimated positions of the face components within detected face boxes. | Eyes and mouthcomponents.
|
Valstar et al. [70],2010 | SVRs are trained to predict the landmark locations using RoI samples. The search is regularized via a Markov network to exploit the learned spatial relationships between landmarks. | A priori probability map of the likely locations of seven fiducial landmarks and the locations of 15 less fiducial landmarks vis-Ã -vis the first seven. | 20 landmarks as in [44].
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