Note: Descriptions are shown in the official language in which they were submitted.
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AUTOMATIC 3D MODELING SYSTEM AND METHOD
Field Of The Invention
The present invention is related to 3D modeling
systems and methods and more particularly, to a system and
method that merges automatic image-based model generation
techniques with interactive real-time character orientation
techniques to provide rapid creation of virtual 3D
personalities.
Background Of The Invention
There are many different techniques for generating
an animation of a three dimensional object on a computer
display. Originally, the animated figures (for example, the
faces) looked very much like wooden characters, since the
animation was not very good. In particular, the user would
typically see an animated face, yet its features and
expressions would be static. Perhaps the mouth would open
and close, and the eyes might blink, but the facial
expressions, and the animation in general, resembled a wood
puppet. The problem was that these animations were
typically created from scratch as drawings, and were not
rendered using an underlying 3D model to capture a more
realistic appearance, so that the animation looked
unrealistic and not very life-like. More recently, the
animations have improved so that a skin may cover the bones
of the figure to provide a more realistic animated figure.
While such animations are now rendered over one or
more deformation grids to capture a more realistic
appearance for the animation, often the animations are still
rendered by professional companies and redistributed to
users. While this results in high-quality animations, it is
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limited in that the user does not have the capability to
customize a particular animation, for example, of him or
herself, for use as a virtual personality. With the advance
features of the Internet or the World Wide Web, these
virtual personas will extend the capabilities and
interaction between users. It would thus be desirable to
provide a 3D modeling system and method which enables the
typical user to rapidly and easily create a 3D model from an
image, such as a photograph, that is useful as a virtual
personality.
Typical systems also required that, once a model
was created by the skilled animator, the same animator was
required to animate the various gestures that you might want
to provide for the model. For example, the animator would
create the animation of a smile, a hand wave or speaking
which would then be incorporated into the model to provide
the model with the desired gestures. The process to
generate the behavior/gesture data is slow and expensive and
requires a skilled animator. It is desirable to provide an
automatic mechanism for generating gestures and behaviors
for models without the assistance of a skilled animator. It
is to these ends that embodiments of the present invention
are directed.
Summary Of The Invention
Broadly, embodiments of the invention utilize
image processing techniques, statistical analysis and 3D
geometry deformation to allow photo-realistic 3D models of
objects, such as the human face, to be automatically
generated from an image (or from multiple images). For
example, for the human face, facial proportions and feature
details from a photograph (or series of photographs) are
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identified and used to generate an appropriate 3D model.
Image processing and texture mapping techniques also
optimize how the photograph(s) is used as detailed, photo-
realistic texture for the 3D model.
In accordance with another aspect of the
invention, a gesture of the person may be captured and
abstracted so that it can be applied to any other model.
For example, the animated smile of a particular person may
be captured. The smile may then be converted into feature
space to provide an abstraction of the gesture. The
abstraction of the gesture (e.g., the movements of the
different portions of the model) are captured as a gesture.
The gesture may then be used for any other model. Thus, in
accordance with some embodiments of the invention, the
system permits the generation of a gesture model that may be
used with other models.
In accordance with one specific aspect of the
invention, there is provided a method for generating a three
dimensional model of an animated object from an image, the
method comprising: determining the boundary of the animated
object to be modeled; determining the location of one or
more landmarks on the animated object to be modeled;
determining the scale and orientation of the animated object
in the image based on the location of the landmarks;
aligning the image of the animated object with the landmarks
with a deformation grid; generating a 3D deformable model of
the animated object based on a mapping of the image of the
object to the deformation grid; and defining a mapping
between the 3D deformable model and a feature space in order
to animate a motion of the 3D deformable model with a
gesture stored in the feature space wherein the gesture can
be applied to a three dimensional model of another object.
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In accordance with another aspect of the
invention, there is provided a computer-implemented system
for generating a three dimensional model of an animated
object from an image, the computer implemented system
comprising: a three dimensional model generation module
further comprising instructions that receive an image of an
animated object and instructions that automatically generate
a three dimensional model of the animated object; and
wherein the instructions that automatically generate a three
dimensional model of the animated object further comprise
instructions that determine a boundary of the animated
object to be modeled, instructions that determine the
location of one or more landmarks on the animated object to
be modeled, instructions that determine the scale and
orientation of the animated object in the image based on the
location of the landmarks, instructions that align the image
of the animated object with the landmarks with a deformation
grid, instructions that generate a three dimensional
deformable model of the animated object based on a mapping
of the image of the animated object to the deformation grid
and define a mapping between the three dimensional
deformable model and a feature space in order to animate a
motion of the three dimensional deformable model with a
gesture stored in the feature space wherein the gesture can
be applied to a three dimensional model of another object.
In accordance with yet another aspect of the
invention, a method for automatically generating an
automatic gesture model is provided. The method comprises
receiving an image of an object performing a particular
gesture and determining the movements associated with the
gesture from the movement of the object to generate a
gesture object wherein the gesture object further comprises
a coloration change variable storing the change of
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coloration that occur during the gesture, a two dimensional
change variable storing the change of the surface that occur
during the gesture and a three dimensional change variable
storing the change of the vertices associated with the
object during the gesture.
In accordance with yet another aspect of the
invention, a gesture object data structure that stores data
associated with a gesture for an object is provided. The
gesture object comprises a texture change variable storing
changes in coloration of a model during a gesture, a texture
map change variable storing changes in the surface of the
model during the gesture, and a vertices change variable
storing changes in the vertices of the model during the
gesture wherein the texture change variable, the texture map
change variable and the vertices change variable permit the
gesture to be applied to another model having a texture and
vertices. The gesture object data structure stored in its
data in a vector space where coloration, surface motion and
3D motion may be used by many individual instances of the
model.
Brief Description Of The Drawings
Figure 1 is a flowchart describing a method for
generating a 3D model of a human face;
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Figure 2 is a diagram illustrating an example of a computer system which may
be used to
implement the 3D modeling method in accordance with the invention;
Figure 3 is a block diagram illustrating more details of the 3D model
generation system
in accordance with the invention;
Figure 4 is an exemplary image of a person's head that may be loaded into the
memory of
a computer during an image acquisition process;
Figure 5 illustrates the exemplary image of Figure 4 with an opaque background
after
having processed the image with a "seed fill" operation;
Figure 6 illustrates the exemplary image of Figure 5 having dashed lines
indicating
particular bound areas about the locations of the eyes;
Figure 7 illustrates the exemplary image of Figure 6 with the high contrast
luminance
portion of the eyes identified by dashed lines;
Figure 8 is an exemplary diagram illustrating various landmark location points
for a
human head;
Figure 9 illustrates an example of a human face 3D model in accordance with
the
invention;
Figures 10A-10D illustrate respective deformation grids that can be used to
generate a 3D
model of a human head;
Figure l0E illustrates the deformation grids overlaid upon one another;
Figure 11 is a flowchart illustrating the automatic gesture behavior
generation method in
accordance with the invention;
Figure 12A and 12B illustrate an exemplary psuedo-code for performing the
image
processing techniques of the invention;
Figures 13A and 13B illustrate an exemplary work flow process for
automatically
generating a 3D model in accordance with the invention;
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Figures 14A and 14B illustrate an exemplary pseudo-code for performing the
automatic
gesture behavior model in accordance with the invention;
Figure 15 illustrates an example of a base 3D model for a first model,
Kristen;
Figure 16 illustrates an example of a base 3D model for a second model, Ellie;
5 Figure 17 is an example of the first model in a neutral gesture;
Figure 18 is an example of the first model in a smile gesture;
Figure 19 is an example of a smile gesture map generated from the neutral
gesture and
the smile gesture of the first model;
Figure 20 is an example of the feature space with both the models overlaid
over each
other;
Figure 21 is an example of a neutral gesture for the second model; and
Figure 22 is an example of the smile gesture, generated from the first model,
being
applied to the second model to generate a smile gesture in the second model.
Detailed Description Of The Preferred Embodiment
While the invention has a greater utility, it will be described in the context
of generating a
3D model of the human face and gestures associated with the human fact. Those
skilled in the
art recognize that any other 3D models and gestures can be generated using the
principles and
techniques described herein, and that the following is merely exemplary of a
particular
application of the invention and the invention is not limited to the facial
models described herein.
To generate a 3D model of the human face, the invention preferably performs a
series of
complex image processing techniques to determine a set of landmark points 10
which serve as
guides for generating the 3D model. Figure 1 is a flow chart describing a
preferred algorithm for
generating a 3D model of a human face. With reference to Figure 1, an image
acquisition
process (Step 1) is used to load a photograph(s) (or other image) of a human
face (for example, a
"head shot") into the memory of a computer. Preferably, images may be loaded
as JPEG images,
however, other image type formats may be used without departing from the
invention. Images
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can be loaded from a diskette, downloaded from the Internet, or otherwise
loaded into memory
using known techniques so that the image processing techniques of the
invention can be
performed on the image in order to generate a 3D model.
Since different images may have different orientations, the proper orientation
of the
image should be determined by locating and grading appropriate landmark points
10.
Determining the image orientation allows a more realistic rendering of the
image onto the
deformation grids. Locating the appropriate landmark points 10 will now be
described in detail.
Referring to Figure 1, to locate landmark points 10 on an image, a "seed fill"
operation
may preferably be performed (Step 2) on the image to eliminate the variable
background of the
image so that the boundary of the head (in the case of a face) can be isolated
on the image.
Figure 4 is an exemplary image 20 of a person's head that may be loaded into
the memory of the
computer during the image acquisition process (Step 1, Figure 1). A "seed
fill" operation (Step
2, Figure 1) is a well-known recursive paintfill operation that is
accomplished by identifying one
or more points 22 in the background 24 of the image 20 based on, for example,
color and
luminosity of the point(s) 22 and expand a paintfill zone 26 outwardly from
the point(s) 22
where the color and luminosity are similar. Preferably, the "seed fill"
operation successfully
replaces the color and luminescent background 24 of the image with an opaque
background so
that, the boundary of the head can be more easily determined.
Referring again to Figure 1, the boundary of the head 30 can be determined
(Step 3), for
example, by locating the vertical center of the image (line 32) and
integrating across a horizontal
area 34 from the centerline 32 (using a non-fill operation) to determine the
width of the head 30,
and by locating the horizontal center of the image (line 36) and integrating
across a vertical area
38 from the centerline 36 (using a non-fill operation) to determine the height
of the head 30. In
other words, statistically directed linear integration of a field of pixels
whose values differ based
on the presence of an object or the presence of a background is performed.
This is shown in
Figure 5 which shows the exemplary image 20 of Figure 4 with an opaque
background 24.
Returning again to Figure 1, upon determining the width and height of the head
30, the
bounds of the head 30 can be determined by using statistical properties of the
height of the head
and the known properties of the integrated horizontal area 34 and top of the
head 30.
30 Typically, the height of the head will be approximately 2/3 of the image
height and the width of
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the head will be approximately 1/3 of the image width. The height of the head
may also be 1.5
times the width of the head which is used as a first approximation.
Once the bounds of the head 30 are determined, the location of the eyes 40 can
be
determined (Step 4). Since the eyes 40 are typically located on the upper half
of the head 30, a
statistical calculation can be used and the head bounds can be divided into an
upper half 42 and a
lower half 44 to isolate the eye bound areas 46a, 46b. The upper half of the
head bounds 42 can
be further divided into right and left portions 46a, 46b to isolate the left
and right eyes 40a, 40b,
respectively. This is shown in detail in Figure 6 which shows the exemplary
image 20 of Figure
4 with dashed lines indicating the particular bound areas.
Refemng yet again to Figure 1, the centermost region of each eye 40a, 40b can
be located
(Step 5) by identifying a circular region 48 of high contrast luminance within
the respective eye
bounds 46a, 46b. This operation can be recursively performed outwardly from
the centermost
point 48 over the bounded area 46a, 46b and the results can be graded to
determine the proper
bounds of the eyes 40a, 40b. Figure 7 shows the exemplary image of Figure 6
with the high
contrast luminance portion of the eyes identified by dashed lines.
Referring again to Figure 1, once the eyes 40a, 40b have been identified, the
scale and
orientation of the head 30 can be determined (Step 6) by analyzing a line 50
connecting the eyes
40a, 40b to determine the angular offset of the line 50 from a horizontal axis
of the screen. The
scale of the head 30 can be derived from the width of the bounds according to
the following
formula: width of bound/width of model.
After determining the above information, the approximate landmark points 10 on
the
head 30 can be properly identified. Preferred landmark points 10 include a)
outer head bounds
60a, 60b, 60c; b) inner head bounds 62a, 62b, 62c, 62d; c) right and left eye
bounds 64a-d, 64w-
z, respectively; d) corners of nose 66a, 66b; and e) corners of mouth 68a, 68d
(mouth line),
however, those skilled in the art recognize that other landmark points may be
used without
departing from the invention. Figure 8 is an exemplary representation of the
above landmark
points shown for the image of Figure 4.
Having determined the appropriate landmark locations 10 on the head 30, the
image can
be properly aligned with one or more deformation grids (described below) that
define the 3D
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model 70 of the head (Step 7). The following describes some of the deformation
grids that may
be used to define the 3D model 70, however, those skilled in the art recognize
that these are
merely exemplary of certain deformation grids that may be used to define the
3D model and that
other deformation grids may be used without departing from the invention.
Figure 9 illustrates
an example of a 3D model of a human face generated using the 3D model
generation method in
accordance with the invention. Now, more details of the 3D model generation
system will be
described.
Figure 2 illustrates an example of a computer system 70 in which the 3D model
generation method and gesture model generation method may be implemented. In
particular, the
3D model generation method and gesture model generation method may be
implemented as one
or more pieces of software code (or compiled software code) which are executed
by a computer
system. The methods in accordance with the invention may also be implemented
on a hardware
device in which the method in accordance with the invention are programmed
into a hardware
device. Returning to Figure 2, the computer system 70 shown is a personal
computer system.
The invention, however, may be implemented on a variety of different computer
systems, such
as client/server systems, server systems, workstations, etc... and the
invention is not limited to
implementation on any particular computer system. The illustrated computer
system may
include a display device 72, such as a cathode ray tube or LCD, a chassis 74
and one or more
input/output devices, such as a keyboard 76 and a mouse 78 as shown, which
permit the user to
interact with the computer system. For example, the user may enter data or
commands into the
computer system using the keyboard or mouse and may receive output data from
the computer
system using the display device (visual data) or a printer (not shown), etc.
The chassis 74 may
house the computing resources of the computer system and may include one or
more central
processing units (CPU) 80 which control the operation of the computer system
as is well known,
a persistent storage device 82, such as a hard disk drive, an optical disk
drive, a tape drive and
the like, that stores the data and instructions executed by the CPU even when
the computer
system is not supplied with power and a memory 84, such as DRAM, which
temporarily stores
data and instructions currently being executed by the CPU and loses its data
when the computer
system is not being powered as is well known. To implement the 3D model
generation and
gesture generation methods in accordance with the invention, the memory may
store a 3D
modeler 86 which is a series of instructions and data being executed by the
CPU 80 to implement
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the 3D model and gesture generation methods described above. Now, more details
of the 3D
modeler will be described.
Figure 3 is a diagram illustrating more details of the 3D modeler 86 shown in
Figure 2.
In particular, the 3D modeler includes a 3D model generation module 88 and a
gesture generator
module 90 which are each implemented using one or more computer program
instructions. The
pseudo-code that may be used to implement each of these modules is shown in
Figures l2A -
12B and Figures 14A and 14B. As shown in Figure 3, an image of an object, such
as a human
face is input into the system as shown. The image is fed into the 3D model
generation module as
well as the gesture generation module as shown. The output from the 3D model
generation
module is a 3D model of the image which has been automatically generated as
described above.
The output from the gesture generation module is one or more gesture models
which may then be
applied to and used for any 3D model including any model generate by the 3D
model generation
module. The gesture generator is described in more detail below with reference
to Figure 11. In
this manner, the system permits 3D models of any object to be rapidly
generated and
implemented. Furthermore, the gesture generator permits one or more gesture
models, such as a
smile gesture, a hand wave, etc:..) to be automatically generated from a
particular image. The
advantage of the gesture generator is that the gesture models may then be
applied to any 3D
model. The gesture generator also eliminates the need for a skilled animator
to implement a
gesture. Now, the deformation grids for the 3D model generation will be
described.
Figures 10A-1 OD illustrate exemplary deformation grids that may be used to
define a 3D
model 70 of a human head. Figure 10A illustrates a bounds space deformation
grid 172which is
preferably the innermost deformation grid. Overlaying the bounds space
deformation grid 172is
a feature space deformation grid 174(shown in Figure lOB). An edge space
deformation grid,176
(show in Figure 10C) preferably overlays the feature space deformation grid
174. Figure 10D
illustrates a detail deformation grid 177 that is preferably the outermost
deformation grid.
The grids are preferably aligned in accordance with the landmark locations 10
(shown in
Figure l0E) such that the head image 30 will be appropriately aligned with the
deformation grids
when its landmark locations 10 are aligned with the landmark locations 10 of
the deformation
grids. To properly align the head image 30 with the deformation grids, a user
may manually
refine the landmark location precision on the head image (Step 8), for example
by using the
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mouse or other input device to "drag" a particular landmark to a different
area on the image 30.
Using the new landmark location information, the image 30 may be modified with
respect to the
deformation grids as appropriate (Step 9) in order to properly align the head
image 30 with the
deformation grids. A new model state can then be calculated, the detail
grid178 can then be
5 detached (Step 10), behaviors can be scaled for the resulting 3D model (Step
11), and the model
can be saved (Step 12) for use as a virtual personality. Now, the automatic
gesture generation in
accordance with the invention will be described in more detail.
Figure 11 is a flowchart illustrating an automatic gesture generation method
100 in
accordance with the invention. In general, the automatic gesture generation
results in a gesture
10 object which may then be applied to any 3D model so that a gesture behavior
may be rapidly
generated and reused with other models. Usually, there may need to be a
separate gesture model
for different types of 3D models. For example, a smile gesture may need to be
automatically
generated for a human male, a human female, a human male child and a human
female child in
order to make the gesture more realistic. The method begins is step 102 in
which a common
feature space is generated. The feature space is conunon space that is used to
store and represent
an object image, such as a face, movements of the object during a gesture and
object scalars
which capture the differences between different objects. The gesture object to
be generated
using this method also stores a scalar field variable that stores the mapping
between a model
space and the feature space that permits transformation of motion and geometry
data. The
automatic gesture generation method involves using a particular image of an
object, such as a
face, to generate an abstraction of a gesture of the object; such as a smile,
which is then stored as
a gesture object so that the gesture object may then be applied to any 3D
model.
Returning to Figure 11, in step 104, the method determines the correlation
between the
feature space and the image space to determine the texture map changes which
represent changes
to the surface movements of the image during the gesture. In step 106, the
method updates the
texture map from the image (to check the correlation) and applies the
resultant texture map to the
feature space and generates a variable "stDeltaChange" as shown in the
exemplary pseudo-code
shown in Figures 14A and 14B which stores the texture map changes. In step
108, the nzethod
determines the changes in the 3D vertices of the image model during the
gesture which captures
the 3D movement that occurs during the gesture. In step 110, the vertex
changes are applied to
the feature space and are captured in the gesture object in a variable
"VertDeltaChange" as
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shown in Figure 14A and 14B. In step 112, the method determines the texture
coloration that
occurs during the gesture and applies it to the feature space. The texture
coloration is captured in
the "DeltaMap" variable in the gesture object. In step 114, the gesture object
is generated that
includes the "stDeltaChange", "VertDeltaChange" and "DeltaMap" variables which
contain the
coloration, 2D and 3D movement that occurs during the gesture. The variables
represent only
the movement and color changes that occurs during a gesture so that the
gesture object may then
be applied to any 3D model. In essence, the gesture object distills the
gesture that exists in a
particular image model into an abstract object that contains the essential
elements of the gesture
so that the gesture may then be applied to any 3D model.
The gesture object also includes a scalar field variable storing the mapping
between a
feature space of the gesture and a model space of a model to permit
transformation of the
geometry and motion data. The scalerArray has an entry for each geometry
vertex in the Gesture
object. Each entry is a 3 dimensional vector that holds the change in scale
for that vertex of the
Feature level from its undefonned state to the deformed state. The scale is
computed by vertex
in Feature space by evaluating the scaler change in distance from that vert to
connected verticies.
The scaler for a given Gesture vertex is computed by weighted interpolation of
that Vertex's
postion when mapped to UV space of a polygon in the Feature Level. The shape
and size of
polygons in the feature level are chosen to match areas of similarly scaled
movement. This was
determined by analyzing visual flow of typical facial gestures. The above
method is shown in
greater detail in the pseudo-code shown in Figure 14A and 14B.
Figures 12A- B and Figures 13A and B, respectively, contain a sample pseudo
code
algorithm and exemplary work flow process for automatically generating a 3D
model in
accordance with the invention.
The automatically generated model can incorporate built-in behavior animation
and
interactivity. For example, for the human face, such expressions include
gestures, mouth
positions for lip syncing (visemes), and head movements. Such behaviors can be
integrated with
technologies such as automatic lip syncing, text-to-speech, natural language
processing, and
speech recognition and can trigger or be triggered by user or data driven
events. For example,
real-time lip syncing of automatically generated models may be associated with
audio tracks. In
addition, real-time analysis of the audio spoken by an intelligent agent can
be provided and
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synchronized head and facial gestures initiated to provide automatic, lifelike
movements to
accompany speech delivery.
Thus, virtual personas can be deployed to serve as an intelligent agent that
may be used
as an interactive, responsive front-end to information contained within
knowledge bases,
customer resource management systems, and learning management systems, as well
as
entertainment applications and communications via chat, instant messaging, and
e-mail. Now,
examples of a gesture being generated from an image of a 3D model and then
applied to another
model in accordance with the invention will now described.
Figure 15 illustrates an example of a base 3D model for a first model,
Kristen. The 3D
model shown in Figure 15 has been previously generated as described above
using the 3D model
generation process. Figure 16 illustrates a second 3D model generated as
described above. These
two models will be used to illustrate the automatic generation of a smile
gesture from an existing
model to generate a gesture object and then the application of that generated
gesture object to
another 3D model. Figure 17 show an example of the first model in a neutral
gesture while
Figure 18 shows an example of the first model in a smile gesture. The smile
gesture of the first
model is then captured as described above. Figure 19 illustrates an example of
the smile gesture
map (the graphical version of the gesture object described above) that is
generated from the first
model based on the neutral gesture and the smile gesture. As described above,
the gesture map
abstracts the gesture behavior of the first model into a series of coloration
changes, texture map
changes and 3D vertices changes which can then be applied to any other 3D
model that has
texture maps and 3D vertices. Then, using this gesture map (which includes the
variables
described above), the gesture object may be applied to another model in
accordance with the
invention. In this manner, the automatic gesture generation process permits
various gestures for
a 3D model to be abstracted and then applied to other 3D models.
Figure 20 is an example of the feature space with both the models overlaid
over each
other to illustrate that the feature space of the first and second models are
consistent with each
other. Now, the application of the gesture map (and therefore the gesture
object) to another
model will be described in more detail. In particular, Figure 21 illustrates
the neutral gesture of
the second model. Figure 22 illustrates the smile gesture (from the gesture
map generated by
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from the first model) applied to the second model to provide a smile gesture
to that second model
even when the second model does not actually show a smile.
While the above has been described with reference to a particular method for
locating the
landmark location points on the image and a particular method for generating
gestures, those
skilled in the art will recognize that other techniques may be used without
departing from the
invention as defined by the appended claims. For example, techniques such as
pyramid
transforms which uses a frequency analysis of the image by down sampling each
level and
analyzing the frequency differences at each level can be used. Additionally,
other techniques
such as side sampling and image pyramid techniques can be used to process the
image.
Furthermore, quadrature (low pass) filtering techniques may be used to
increase the signal
strength of the facial features, and fuzzy logic techniques may be used to
identify the overall
location of a face. The location of the landmarks may then be determined by a
known corner
finding algorithm.
