Automatic Creation of A Talking Head
From A Video Sequence
Author :
KyoungHo Choi , Jenq-Neng Hwang
Presented by: Rubie F. Viñas, 方如玉
Adviser: Dr. Shih-Chung Chen, 陳世中
Date: December 31, 2008
Presented
By
STU Institute
of Electrical
Engineering
Harry Mills / PRESENTATIONPRO
Outline
Introduction
Facial Feature Extraction
Face Shape Extractor
Probability Networks
A Least-Square Approach to Adopt a 3D Face Model
Implementation and Experimental Results
Conclusion and Future Work
References
Presented
By
STU Institute
of Electrical
Engineering
Harry Mills / PRESENTATIONPRO
Abstract
•
Paper
– Real-time
System
Extract
2D
– Talking Head
Video
Sequence
• Video
Sequence
Facial
Features
•
Automatic
–
No user intervention
Probabilistic
Network
3D Face
Model
Audio to
Visual
Conversion
Technique
Talking
Head
Presented
By
STU Institute
of Electrical
Engineering
Harry Mills / PRESENTATIONPRO
I.
Introduction
Presented
By
STU Institute
of Electrical
Engineering
Harry Mills / PRESENTATIONPRO
Introduction
• Advances in computing power and numerical algorithms in
graphics and image-processing.
– Build a realistic 3D face
• From
– Video Sequence
» Using a regular PC camera.
• Needs user intervention
– Initialization stage
• Provide feature points to generate a photo realistic 3D face model.
– Two orthogonal frames
– Multiple frames
• Techniques
– Build high quality face models
• Computationally Expensive
• Time Consuming
Presented
By
STU Institute
of Electrical
Engineering
Harry Mills / PRESENTATIONPRO
Introduction (cont.)
• Integrating Talking Heads
– Highly required to enrich their human-computer interface.
– Various Multimedia Applications
• Video Conferencing
• E-commerce
• Virtual Anchors
• Recent Researches
– Talking head that do not require high quality animation.
– Fast and easy way to build a 3D face
• Generate many different face models in a short period of time.
– User intervention required
• Provide several corresponding points
– Two frames from a video sequence
Presented
By
of Electrical
Engineering
– Feature points in a single frontal image STU Institute
Harry Mills / PRESENTATIONPRO
Approaches for Creating a 3D Face Model
1. Use a generic 3D Face Model
–
–
3D Scanner
Deform the 3D Model by calculating coordinates of all vertices in the 3D Model.
1. Lee
–
–
Deformation of vertices in a 3D model as an interpolation of the displacements of the given control points.
Dirichlet Free-Form Deformation Technique
» Calculate new 3D coordinates of a deformed 3D model.
2. Pighin
–
Model deformation as an interpolation problem and used radial basis functions to find new 3D coordinates
for vertices in a generic 3D model.
2. Use multiple 3D Face Models
–
–
Find 3D coordinates of all vertices in a new 3D Model based on given feature points.
Combine multiple 3D Models to generate a new 3D Model by calculating parameters to
combine them.
1. Blanz
–
Laser Scanner (CyberwareTM)
» Generate a 3D Model database.
» Considered a new face model as a linear combination of the shapes of 3D faces in the database.
–
Simplified the idea of linear combination of 3D models
» Designing key 3D faces that can be used to build a new 3D model by combining the key 3D faces
linearly, eliminating the need for a large 3D face database.
2. Liu
–
Merit
•
Linearly created face objects can eliminate a wrong face that is not natural.
–
Very important aspect to create a 3D face model without user intervention.
Presented
By
STU Institute
of Electrical
Engineering
Harry Mills / PRESENTATIONPRO
Approaches for Single Image Used
•
3D Face Model
–
–
Computationally cheap and fast
Suitable to generate multiple face models for a short time
1.
Valle
•
Manually Extracted
•
Interpolation technique
–
–
2.
Anthropometric and apriori information
–
Estimate the depth of a 3D face model.
Lin
•
2D Mesh Model
–
–
•
Radial basis function
»
Obtain coordinates of polygon mesh of a 3D model.
Kuo
•
3.
18 feature points
Animate a talking head
»
Mesh Warping
Manually adjust control points of a mesh to fit eyes, nose and mouth into an input image.
Problem:
–
Depth Information of the created 3D Model
•
Not as accurate as other labor intensive approaches.
Presented
By
STU Institute
of Electrical
Engineering
Harry Mills / PRESENTATIONPRO
Paper
• Real-time System from a Video Sequence
– Automatically builds a 3D face model
• No user input needed
• Objectives
– To present a real-time system that extracts facial features
automatically and builds a 3D face model without user
intervention.
•
•
•
•
Face Shape Extractor
Probabilistic Network
A Least-Square Approach
Talking-Head System
Presented
By
STU Institute
of Electrical
Engineering
Harry Mills / PRESENTATIONPRO
II.
Facial Feature Extraction
Presented
By
STU Institute
of Electrical
Engineering
Harry Mills / PRESENTATIONPRO
Finding Rough Location
•
–
–
•
Find rough location of target feature points.
High energy around the facial features
Facial Feature
–
Deep Valley for their luminance distribution.
•
•
•
•
Test Images (Front View)
2D Valley Detection Filter
Eyebrows
Eyes
Mouth
Valley Energy Equation:
Ev ( x0 )  max {dR( x0 , x)  dL( x0 , x)    abs(dR( x0 , x)  dL( x0 , x))},
Results of a Valley Detection Filter
x l to k
•
Where:
dR( x0 , x)  f ( x0  x)  f ( x0 )
dL( x0 , x)  f ( x0  x)  f ( x0 )
d
dR
x0
x
x
Valley Detection Filter
Horizontal Direction
Vertical Direction
Histogram Distribution
Presented
By
STU Institute
of Electrical
Engineering
Harry Mills / PRESENTATIONPRO
Produces high intensity band for an object boundary
and changes the region around the object into a low
intensity region
Finding Exact Location
Tff  thresholding
Performed to make a two-tone image
Moving Object
•
Pseudo Moving Difference
– Contour extraction method
• Motion Estimation
• Moving Object Segmentation
Intensified Band at the
boundary
Homogeneous
– Moving Difference Image
• Two images
– Image 1  time T1
– Image 2  time T2
Synthetic Object
• Equation
– No need to create a new image to
take a difference from it.
– Where:
» mx, my  movement in the x
and y direction.
– Object not actually moving.
Difference Image
Simple Contour Extraction
d mx,my  x, y   f  x, y   f  x  mx , y  my 
Strong Intensity band
around the boundary
Presented
By
STU Institute
of Electrical
Engineering
Harry Mills / PRESENTATIONPRO
Mouth Region
0 pixel moved
1 pixel moved down 4 pixels moved down
Finding Exact Location (cont.)
• Mouth and Eye model to extract outside contours
Applied Contour Model
– Parametric Curve
• Mouth
• Eyes
– Search p1, p2 and h1, h2
• Maximize the energy of intensity of pixels on the curve
Example Image of thresholding after the pseudo moving difference
Binarization (Tff=80) result of a moving difference
Extracted Mouth Contour
Use: Parametric Curve
Presented
By
STU Institute
of Electrical
Engineering
Harry Mills / PRESENTATIONPRO
III. Face Shape Extractor
Presented
By
STU Institute
of Electrical
Engineering
Harry Mills / PRESENTATIONPRO
Face Shape
•
•
One of the most important features in creating a 3D model.
Classification of Face Shapes:
Square
Triangular
Trapezoid
Long Narrow
Move Up
•
Same Size
Width and Height
Move Down
Use Ellipse
– Controlled by three anchor points: P1,P2,P3
Presented
By
STU Institute
of Electrical
Engineering
Harry Mills / PRESENTATIONPRO
Three Anchor Points Detection
Tfs  0.5* Intensity of the inside of a face
Step 1:
Step 2:
Step 3:
Edge Image
Find the three Anchor Points.
Draw an Ellipse
P1  180° (move up/down)
P2  270° (fixed position)
x2 y2

1
a2 b2
P3  0° (move up/down)
Where: Face Shape (Symmetric)
Add the intensity of pixels
that are lower than the left
and right anchor points on
the ellipse and record the
sum.
Move the left and right
anchor points up and down
to find the parameters of
the ellipse that produces
maximum boundary energy
for the face shape from the
edge of the image.
a distance b/w P1 and P2 (x)
b distance b/w P1 and P2 (y)
Optimal Face Shape:
Where:
E(x,y)  Intensity of an Edge Image
el  Subset of pixels on an Ellipse
 Pixels located lower than the left and right anchor points
(aˆ, bˆ)  arg max
a ,b

E (a 1  ( y / b) 2 , b 1  ( x / a) 2 )
( x , y )el
such that P1 , P2 , P3  el ,
Presented
By
STU Institute
of Electrical
Engineering
Harry Mills / PRESENTATIONPRO
IV. Probability Networks
Presented
By
STU Institute
of Electrical
Engineering
Harry Mills / PRESENTATIONPRO
Literature Review
•
Uses
– Locate human faces from a scene
– Track deformations of local features
•
Approach
1. Cipolla
•
Probabilistic framework to combine different facial features
and face groups.
– Achieving a high confidence rate for face detection from a
complicated scene.
2. Huang
•
Probabilistic Network for local feature tracking.
– Modeling locations and velocities of selected feature points.
Presented
By
STU Institute
of Electrical
Engineering
Harry Mills / PRESENTATIONPRO
Probabilistic Framework
• Maximally use facial feature evidence
– Deciding correctness of extracted facial features
• Before a 3D face model is built.
3.14
3.13
3.5 3.8 3.113. 3.7
3.123.10
63
.
10.8
10.7
9.15
8.4 2.2 8.1 9 8.3
2.3
8.2
2.1
Probabilistic Framework FDP’s
Each Node  Random Variable
Each Row  Conditional dependency
between two nodes
Network Hierarchy
Face Shape Net
Mouth Net
Eye
Presented
By
STU Institute
of Electrical
Engineering
Harry
Mills
/
PRESENTATIONPRO
Net
Topology Net
V.
A Least-Square Approach to Adopt A
3D Face Model
Presented
By
STU Institute
of Electrical
Engineering
Harry Mills / PRESENTATIONPRO
Approach
•
•
Algorithm that is robust and stable that is used to build a photo-realistic and natural
3D face model.
Literature Review
–
Liu
•
•
Combination of multiple 3D models linearly is a promising way to generate a photo-realistic 3D
model
Paper Approach
–
–
New face model is described as a linear combination of key 3D face models.
Strong Point
•
Multiple face models constrain the shape of a new 3D face
–
–
Similarity with Liu
•
–
Preventing algorithms from producing an unrealistic 3D face model.
3D model  Linear combination of a neutral face and some deformation vectors.
Difference with Liu
•
Use a Least-Square approach to find the coefficient vector for creating anew 3D face model
–
•
Rather than using an iterative approach
Build a 3D Face model from a video sequence with no user input.
Presented
By
STU Institute
of Electrical
Engineering
Harry Mills / PRESENTATIONPRO
3D Model
•
Modified version of the 3D Face Model
– Developer
• Parke and Waters
•
3D Model Editor
– Build a complete head and shoulder model
• Including
– Ears
– Teeth
m
F  F0   ci D i
i 1
Where:
F  Face Geometry
F0  Neutral Face Vector
c  Coefficient Vector
c=(c1,c2,…,cm)
*Decides the amount of variation needed to be applied
to vertices on the neutral face model.
•
•
16 Face Models
T
Face Geometry F  (v1 ,..., vn )
– Where:
vi  ( xi , yi , zi )T
•
T
Deformation Vector D  ( v1 ,...,  vn )
– Amount of variation for size and location of vertices on a 3D model
Presented
By
STU Institute
of Electrical
Engineering
Harry Mills / PRESENTATIONPRO
3D Model Adaptation
•
All coefficients are decided at once
To solve the Least-square Problem
• Use the SVD
– Least-square
• Singular Value Decomposition
• Where
cˆ  arg min
c
n
[(V
j 1
j
Deformation Vectors, D
m
 F0 j )   ci Dij ]2 ,
i 1
– n  Denotes the number of extracted features
– m  Number of deformation vector D
– Vj  Extracted Feature
» Contains x and y location
– F0j, Dij  Vertex on a neutral 3D model
» Projected onto 2D using current camera parameters
 Corresponding vertex on a deformation vector Di
-
=
8 Shape Vectors:
1.
Wide Face
2.
Thin Face
3.
Big Mouth
4.
Small Mouth
5.
Big Nose
6.
Small Nose
7.
Big Eyes
8.
Small Eyes
8 Position Vectors:
1.
Eyes Minimum Horizontal
2.
Eyes Maximum Horizontal
3.
Eyes Minimum Vertical
4.
Eyes Maximum Vertical
5.
Mouth Minimum Vertical
6.
Mouth Maximum Vertical
7.
Nose Minimum Vertical
8.
Nose Maximum Vertical
Deformation Vector for a Wide Face
Presented
By
STU Institute
of Electrical
Engineering
Harry Mills / PRESENTATIONPRO
3D Face Models to Find Deformation Vectors
Neutral 3D Face Model
3D Face Models used to calculate deformation vectors
1294 Polygons
Good enough for realistic facial animation
Presented
By
STU Institute
of Electrical
Engineering
Harry Mills / PRESENTATIONPRO
Procedure to Adapt a Generic Model from a Video Sequence
Video
Sequence
m 3D models
…
…
Feature extraction is OKed by
probability networks
c1
c2
…
cm
Facial Features
Least-Square Approach to obtain a
new 3D model
Orthogonal Projection to
get texture
Texture and its
coordinates
Linearly combined
*To generate a new 3D model according to
a Coefficient Vector calculated by a
Least-Square Approach based on
extracted facial features
Vertices on the new 3D model are projected
into the input face to get face texture and
texture coordinates
Rendered image from the
adapted 3D and texture map
Presented
By
STU Institute
of Electrical
Engineering
Harry Mills / PRESENTATIONPRO
VI. Implementation and Experimental
Results
Presented
By
STU Institute
of Electrical
Engineering
Harry Mills / PRESENTATIONPRO
Automatic Creation of a 3D Face Model
• Characterization of face movement in the input video sequences.

y
Algorithm
*Catches the best facial orientation
*Extracting and Verifying facial feautures

x
Input Face
Translated and Rotated
α°  x-axis
β°  y-axis
•
Samples of Input Video Sequences
•Neutral Face
•Looking at the camera
•Rotating in the x and y axis
Requirements for the Real-Time System (Analyzing Video Sequence)
1. Locating face should not be called every frame.
2. Facial features in previous frames should be exploited.
–
Provide a better result in current frame.
Presented
By
STU Institute
of Electrical
Engineering
Harry Mills / PRESENTATIONPRO
Method: Normalized RG Color Space
*Not called every frame
*Best Facial Orientation
INPUT
Block Diagram of the Real Time System
Valley Detection Filter
*Histogram Distribution
*Rough positions of facial components
Increase Threshold
Nose
*Recursive Thresholding
Nose Holes lowest intensity Around the nose
Mouth and Eyes
*Pseudo Moving Difference
Ellipse Search Area
*Face Shape Extractor
Extracted Features
Correctness and Suitability
*Quality Control Agent
Build 3D Model
Presented
By
STU Institute
of Electrical
Engineering
Harry Mills / PRESENTATIONPRO
Speech-Driven Talking Head System
• Block Diagram of the Encoder
Constrained Optimization
*Robust in Noise Environment
Decoder
Via Internet
bit
CameraPC
Obtained automatically from a
video sequence
FDP  Facial Definition Parameter
FAP  Facial Animation Parameter
Presented
By
STU Institute
of Electrical
Engineering
Harry Mills / PRESENTATIONPRO
Transmitting Speech Via Internet
•
G.723.1
–
–
Dual rate speech coder for multimedia communications.
Most widely used standard codec for Internet telephony
•
Capability of low bit rate coding
–
–
•
5.3kbps
6.3kbps
Implementation
1. 3D coordinates and texture information for an adapted 3D model
•
Sent to the decoder via TCP protocol
2. Coded speech and animation parameters
•
•
Sent to the decoder via UDP protocol
Selected Bit Rate for Audio and Visual Parameter Transmission
–
–
6.3kbps
1.0kbps
Presented
By
STU Institute
of Electrical
Engineering
Harry Mills / PRESENTATIONPRO
Encoder and Decoder Implementation
Encoder
Input Video
Decoder
Virtual Face
Talking Head Window
•Decoded Animation Parameter
•Speech
Presented
By
STU Institute
of Electrical
Engineering
Harry Mills / PRESENTATIONPRO
Experimental Results
•
Requirements
– Face Shape
•
•
•
•
9 fps
Pentium III 600 MHz PC
n=20 Feature Points
m=16 Deformation Vectors
– User
•
•
Face Shape Extractor
– Test
•
Different
– Face Shape
– Orientation
– Detection Rate
•
64%
– 1180 selected Frames
» Testing Video Sequence
Frontal View
– Rotation Angle < 5°
– 20 Video Sequence Recorded
•
2000 Frames in total
Presented
By
STU Institute
of Electrical
Engineering
Harry Mills / PRESENTATIONPRO
Experimental Results
• Errors
Rejected Facial Features
Complex Background
Failure of Eye Detection
Eye Closing
• Probabilistic Network
Increased Extraction Accuracy
– 50 Frontal Face Images
• PICS Database
– University of Stirling
» http:// pics.psych.stir.ac.uk/
– Quality Control Agent
• 2D Gaussian Distributions
Adjust Threshold:
Tff: 80  60
Tfs: 0.5*Ave I  1*Ave I
– Expectation Maximation Algorithm (EM)
Presented
By
STU Institute
of Electrical
Engineering
Harry Mills / PRESENTATIONPRO
Created 3D models
• Talking Head System
– Performance
 Evaluated Successfully
– 20 people participated
– 5-Point Scale
Animating
Talking Head
Assessment
3D models wearing cloth1
Score
Bad=1, Excellent=5
1
2
3
4
5
Photo-realistic
0
0
0
4
8
Natural
0
0
5
7
0
Audio Quality
0
0
0
12
0
Synchronization
0
0
2
10
0
Overall Performance
0
0
1
11
0
AUTOMATIC SCHEME
Produces a 3D Model that is quit realistic and
good enough for various internet applications.
3D models wearing cloth2
Presented
By
STU Institute
of Electrical
Engineering
Harry Mills / PRESENTATIONPRO
VII. Conclusions and Future Works
Presented
By
STU Institute
of Electrical
Engineering
Harry Mills / PRESENTATIONPRO
Conclusions
•
System
 Extract Face Shape
• Ellipse model controlled by three anchor points.
• Accurate and computationally cheap method.
 Probabilistic Network
• Verify if the extracted features are good enough to build a 3D face model.
 Least-Square Approach
• Adopt a generic 3D model into extracted features from input video.
• Calculate a required coefficient vector to adapt a generic model to fit an input
face.
 Talking-Head System
• Generates FAP’s and FDP’s for MPEG-4 facial animation system.
• No user intervention
• Internet Application
– Virtual Conference
– Virtual Story Teller
• Do not require much head movement
• Do not require high quality facial animation
Presented
By
STU Institute
of Electrical
Engineering
Harry Mills / PRESENTATIONPRO
Future Works
1. More accurate mouth and eye extraction scheme.
– Improve quality of the created 3D model.
2. Handle input faces that are not just neutral.
3. Remove Limitation
– Shape of the mouth and eye.
4. Add modeling of hair.
Presented
By
STU Institute
of Electrical
Engineering
Harry Mills / PRESENTATIONPRO
VIII. References
Presented
By
STU Institute
of Electrical
Engineering
Harry Mills / PRESENTATIONPRO
References A
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
Won-Sook Lee, Marc Escher, Gael Sannier, Nadia Magnenat-Thalmann, "MPEG-4 Compatible Faces from Orthogonal Photos,"
International Conference on Computer Animation, 1999, pp.186-194.
P. Fua and C. Miccio, “Animated Heads from Ordinary Images: A Least-Squares Approach,” Computer Vision and Image
Understanding, Vol. 75, No. 3, 1999, pp. 247-259.
Frederic Pighin, Richard Szeliski, David H. Salesin, “Resynthesizing Facial Animation through 3D Model-Based Tracking,” The
Proceedings of the Seventh IEEE International Conference on Computer Vision, Volume 1, 1999, pp. 143 –150.
Zicheng Liu, Zhengyou Zhang, Chuck Jacobs, Michael Cohen, “Rapid Modeling of Animated Faces From Video,” Technical
Report MSR-TR-2000-11.
Ana C. Andres del Valle and Jorn Ostermann, “3D Talking Head Customization By Adapting a Generic Model to One
Uncalibrated Picture,” IEEE International Symposium on Circuits and Systems, 2001, pp. 325 –328.
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on CSVT, vol. 12, no. 3, 2002, pp. 183-192.
Moccozet L., Magnenat Thalmann N., “Dirichlet Free-Form Deformations and their Application to Hand Simulation,” Proc. Of
Computer Animation 97, 1997, pp. 93-102.
V. Blanz and T. Vetter, “A Morphable Model for the Synthesis of 3d Faces,” In Computer Graphics, Annual Conference Series,
SIGGRAPH 1999, pp. 187-194.
Eric Cosatto and Hans Peter Graf, “Photo-Realistic Talking-Heads from Image Samples,” IEEE Transactions on Multimedia, vol.
2, no. 3, 2000, pp. 152-163.
I-Chen Lin, Cheng-Sheng Hung, Tzong-Jer Yang, Ming Ouhyoung, “A Speech Driven Talking Head System Based on a Single
Face Image,” Seventh Pacific Conference on Computer Graphics and Applications, 1999, pp. 43 –49.
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Ru-Shang Wang, Yao Wang, “Facial feature extraction and tracking in video sequences,” IEEE International Workshop on
Multimedia Signal Processing, 1997, pp. 233 –238.
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Pattern Recognition, 1992, pp. 117 –120.
M. Zobel, A. Gebhard, D. Paulus, J. Denzler, H. Niemann, “Robust facial feature localization by coupled features,” Fourth IEEE
International Conference on Automatic Face and Gesture Recognition, 2000, pp. 2 –7.
Y. Tian, T. Kanade, and J. Cohn, “Robust Lip Tracking by Combining Shape, Color and Motion,” 4th Asian Conference on
Computer Vision, 2000.
Presented
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Engineering
Harry Mills / PRESENTATIONPRO
References B
16.
17.
18.
19.
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26.
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28.
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Systems Group Report No. 95/44, University of Sheffield, UK, 1995.
Changick Kim and Jenq-Neng Hwang, "An Integrated Scheme for Object-Based Video Abstraction," ACM
International Multimedia Conference, 2000.
Lesilie G. Farkas. Anthropometry of the Head and Face. Raven Press, 1994.
K. C. Yow and R. Cipolla, “A Probabilistic Framework for Perceptual Grouping of Features for Human Face
Detection,” The Proceedings of IEEE Int. Conf. On Auto. Face and Gesture Recognition ’96, pp. 16-21, 1996.
Hai Tao, Lopez R., Thomas Huang, “Tracking Facial Features Using Probabilistic Network,” Automatic Face and
Gesture Recognition, pp. 166-170, 1998.
ISO/IEC FDIS 14496-1 Systems, ISO/IEC JTC1/SC29/WG11 N2501, November, 1998.
ISO/IEC FDIS 14496-2 Visual, ISO/IEC JTC1/SC29/WG11 N2502, November, 1998.
Psychological Image Collection at Stirling (PICS). Available at http://pics.psych.stir.ac.uk/.
J. Luettin, N.A. Tracker, and S. W. Beet, “Active Shape Models for Visual Speech Feature Extraction,” Electronic
Systems Group Report No. 95/44, University of Sheffield, UK, 1995.
Y. Tian, T. Kanade, and J. Cohn, “Robust Lip Tracking by Combining Shape, Color and Motion,” 4th Asian
Conference on Computer Vision, 2000.
K.H. Choi and Jenq-Neng Hwang, "Creating 3D Speech-Driven Talking Heads: A Probabilistic Approach," to appear
in IEEE International Conference on Image Processing, 2002.
Fabio Lavagetto, “Converting Speech into Lip Movement : A Multimedia Telephone for Hard of Hearing People,”
IEEE Transaction on Rehabilitation Engineering, vol. 3, no. 1, 1995, pp. 90-102.
Ram R. Rao, Tsuhan Chen, Russell M. Mersereau, "Audio-to-Visual Conversion for Multimedia Communication,"
IEEE Transactions on Industrial Electronics, vol. 45, No. 1, 1998, pp. 15-22.
Frederic I Parke, Keith Waters, Computer Facial Animation, A.K. Peters, 1996.
Dual rate speech coder for multimedia communications transmitting at 5.3 and 6.3 kbits/s,” ITU-T Recommendation
G.723.1, March 1996.
Presented
By
STU Institute
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Engineering
Harry Mills / PRESENTATIONPRO
Presented
By
STU Institute
of Electrical
Engineering
Harry Mills / PRESENTATIONPRO