List of Tables
3D Pose Estimation and
List of Figures
3D Normalization as a Bridge Between Feature Detection and Face Recognition
Sobel edge detection
Deriche edge detection at multiple scales
Typical output from Sobel edge detection
Post-Processing Sobel edge detection
An input image pyramid for multi-scale edge extraction
Multi-Scalar edge extraction with the Sobel operator
Cocircularity of edges
The set of circular sampling regions, the set of symmetry orientations and the set of cocircular edge orientations
Discarding edges misaligned with the annulus normals
Input to the attentional mechanism
Lines of general symmetry at multiple scales
Combining lines of symmetry
The semi-elliptical model
A sample template for face or head-like blob detection with template normals represented with intensity
Projecting onto template normals to attenuate misaligned edges
Splitting templates into angular bins
Computing the angular profile of a contour
Symmetric enclosure versus traditional template matching
The hierarchical search sequence for faces and facial features
The multi-scalar interest map pyramid
The search space for the selective symmetry detector
Insufficient operator overlap problems
The face templates used by the selective symmetry detector
The collection of detected possible facial contours
Generating eye search region from the facial contour
Isolating the eye search regions
Eye operator size versus face size
Isolating the eye search regions
Strong symmetry responses from structures other than eyes
Eye midpoint not centered in facial contour
Minimum intra-ocular distance
Horizontal Projection of Symmetry Points
Excessive limb undulation
Search space for seeding mouth limb extraction
Search space for mouth superimposed on face
Limb axes extracted as 3D trajectories
Intensity variance with mouth locus
The segmented mouth limb
Nose edge data
Finding the nose tip from the nose bottom using the edge signature.
The nose-bottom-line and the nose-tip-line.
A typical output of the face detection stage.
In-plane rotation, scaling and translation.
Out-of-plane or depth rotations.
Some of the 3D faces used to form the average 3D face.
The average 3D face
Image of U.S. President Ford with eyes, nose and mouth located
The scaled orthographic projection of the model upon the image plane
Stretching the model in search of the best mouth fit.
The 3D model coated with an intensity image's face.
Mirroring intensity images from one side of the face to the other.
Range of nose positions where mirroring is necessary.
Some re-projections of the coated 3D model.
A synthesize mug-shot image of U.S. President Ford.
Histogram of the mean face.
Windowing to split face histogram correction for each side of the face.
Limiting histogram generation to avoid hair and beards.
The mixture of histogram mappings from the left to the right side of the face.
The mean face.
The ordered eigenvalues of the dataset.
The ordered eigenvectors (or eigenfaces) of the dataset.
The distribution of the dataset in the first three coefficient dimensions.
The mean face generated with smaller mug-shots.
The distribution of first two coefficients and the residue (on the vertical axis) for the dataset.
Mug-shots containing true faces and non-faces and a graph of their distance to face space (DFFS) values.
The nose localization problem.
Distance to face space values for mug-shot images from different nose-point trials (from left to right across the nose-line)
The final localization.
Block diagram description of the overall algorithm
The video output for a sample image
The video output for a sample camera snapshot
The 30 individuals in the Achermann database
The 10 different views per individual in the database
The recognition video output
The recognition rates for different views
The recognition rates for different individuals
Synthesized mug-shots with left eye anchor point perturbed
KL approximations to mug-shots with left eye anchor point perturbed
Variation in recognition certainty under left eye anchor point perturbation
Variation in recognition certainty under right eye anchor point perturbation
Variation in recognition certainty under nose anchor point perturbation (View 1)
Variation in recognition certainty under nose anchor point perturbation (View 2)
Variation in recognition certainty under mouth anchor point perturbation