Low Bandwidth Video Streaming using FACS, Facial Expression and

Animation Techniques

Dinesh Kumar and Jito Vanualailai

School of Computing, Information and Mathematical Sciences, The University of the South Pacific, Suva, Fiji

Keywords: Facial Expression, Facial Animation, Low Bandwidth, Video Streaming, FACS.

Abstract: In this paper we describe an easy to use real-time 3D facial expression and animation system that takes the

creation of individual facial expressions to the atomic level. That is instead of generating and recording

known facial expressions we propose a mechanism that will allow us to create and store each atomic facial

distortion. We can then combine some of these singular distortions to create meaningful expressions. FACS

Action Units (AUs) is one such technique that describes the simplest visible movement, which cannot be

decomposed into more basic ones. We use this as the basis for creating these atomic facial distortions. The

Waters muscle based facial model has been used and extended to allow the user to calibrate and record each

facial deformation as described in FACS AUs. The user can then create any facial expression by simply

stating the series of AUs and its degree of activation in a controlled fashion. These features all form part of

the Facial Animation System (FAS). Our FAS is implemented in such a way that enables it to be used as a

low bandwidth video streaming player - a real time facial animation player driven only by FACS AUs

transmitted as plain text over TCP sockets.

1 INTRODUCTION

In this paper we study the Facial Action Coding

System (FACS) as described in (Ekman and Frieson,

1977) and its application to facial expression

animation. We also investigate the area of facial

animation by examining some common facial

modeling and animation techniques and

implementing a Facial Animation System (FAS). In

FAS we demonstrate the application of the FACS

standard on a 3D face model based on Waters

muscle model (a popular technique in facial

expression creation described in (Waters, 1987)).

The animation of the face therefore is controlled by

incoming stream of FACS Action Unit (AU)

parameters.

This ties our system closely with the already

available information about human facial behaviour

given in FACS. The computational cost of facial

animation has been solved with the advent of high

processing power and advanced graphics rendering

capabilities of modern computers. What is left

therefore is the creation of a human face model and

a novel way to animate the model from one state to

another. At this point we realize that the first step in

the animation process is creation of a synthetic face

model. There are many commercial 3D modeling

software that allows development of 3D face models

for animation. However this process is rather tedious

and very time consuming.

Next step is the creation of individual facial

expressions. Each expression therefore describes a

human emotion such as smile, anger and surprise

etcetera. In many facial animation systems

developed the user simply creates an expression by

tweaking parameters that directly deforms parts of

the synthetic face model. In other words basic

human expressions are pre-programmed into the

system. The resultant expression is normally

validated through visual inspection. These

expressions are then sent to animation engine.

Therefore most facial animations systems lack a

systematic way for creation of these facial

expressions.

This situation motivated our research. We

propose a 3D facial expression and animation

system that takes the creation of individual facial

expressions to the atomic level. That is instead of

generating and recording known expressions we

propose a mechanism that will allow us to create and

store each atomic facial distortion. We can then

combine some of these singular distortions to create

226

Kumar, D. and Vanualailai, J.

Low Bandwidth Video Streaming using FACS, Facial Expression and Animation Techniques.

DOI: 10.5220/0005718202240233

In Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2016) - Volume 1: GRAPP, pages 226-235

ISBN: 978-989-758-175-5

meaningful expressions. We use FACS AUs as the

basis for creating atomic facial distortions. In

summary the key contributions made to the field in

this paper are:

 the ability of FAS to create atomic facial

distortions based on information about human

facial behaviour described in FACS with ease.

 a technique to execute a set of AUs to generate

an expression.

 a simple technique proposed to apply the degree

of activation of AUs in expression creation.

 development of a simple animation engine for

simulation.

 implementation of a low bandwidth video

streaming system; a real-time facial animation

player driven only by FACS AUs transmitted as

plain text over TCP sockets.

It is therefore envisaged that our system can prove

useful to researchers studying the relationship

between the generating of facial expression using

FACS AUs as the basis. We wish to demonstrate the

feasibility of using the FACS paradigm for the

animation of facial expressions hence we selected a

subset of AUs and implemented their behaviour. In

this research we also attempt to understand some of

the principles that are used to model face

expressions based on FACS and implement a basic

animation system on these. This paper therefore

deals with FACS and animation but does not cover

FACS AU recognition.

The rest of the paper is organised as follows: In

section 2 we review related work in the field of

FACS driven facial expression and animation.

Section 3 describes the architecture of our system

and in section 4 we describe the components of our

FAS and algorithms developed. In section 5, we

present the experimental results followed by

conclusion in section 6.

2 RELATED WORK

The human face is a complex architecture which is

capable of creating a vast range of facial expressions

on the fly. Not only are the same named expressions

different from person to person, mimicking the same

on a computer has remained a challenge to computer

scientists for over several decades now. The primary

focus had been to create models capable of

rendering high quality and realistic facial

expressions. Initial efforts in 3D facial modeling

began with (Parke, 1972) who developed the first

parameterized facial model. (Waters, 1987) and

(Magnenat-Thalmann et al., 1988) follow soon by

developing pseudo muscle based models. The

technique of free form deformations were also used

to create facial expressions for example in (Kalra et

al., 1992). (Kahler et al., 2001) utilized geometry

based muscle modeling for facial animation where

as (Ostermann, 1998) used the MPEG-4 standard to

animate synthetic faces.

It is to be noted that the state of the art in facial

animation has advanced tremendously over the

years. For instance there is increasing effort by

scientists to perfect the facial model to produce

quality and realistic expressions. Example (Tena et

al., 2011) developed an interactive region-based

linear 3D face model that effectively divided the

facial model into regions and used user-given

constraints (markers placed on the face model).

When activated these markers affected only the

region in which they were placed and produced

better effects than using the same markers on a

region less model. The practice and theory of

blendshape models has been well discussed by

(Lewis et al., 2014). (Weise et al., 2011) discusses

real time performance based facial animation that

enables the user to control the facial expression of a

digital avatar in real-time. Similar facial

performance based models were proposed by (Pauly,

2013) and (Bermano et al., 2013). On the other hand

there is equal effort by scientists to produce facial

animation from video or motion capture data.

Techniques have been developed to detect facial

expressions using motion detectors or sensors that

are susceptible to movements and then animated on

a computer facial model. One such implementation

is described by (Sifakis et al., 2005) who were able

to automatically determine muscle activations from

motion capture marker data.

It is therefore evident that most of these

researches were focused on perfecting models to

generate expressions and the results validated by

FACS. Instead in our research we propose a method,

which is to use FACS to generate expressions rather

than to just use it to validate expressions. Some

research is evident in this area such as in (Alkawaz

et al., 2015), (Wojdel and Rothkranz, 2005) and

(Wojdel and Rothkranz, 2001). (Wojdel and

Rothkranz, 2005) in their research developed a

parametric performance based model where each

parameter corresponded to one of the AUs as

defined in FACS.

They also developed co-occurrence rules that

described how different AUs influenced each other.

We employ the co-occurrence rules developed by

(Wojdel and Rothkranz, 2005) in our paper, but

Low Bandwidth Video Streaming using FACS, Facial Expression and Animation Techniques

227

Figure 1: Architecture of our Facial Animation System (FAS).

instead we use these rules to generate expressions on

Waters muscle model rather than on a parametric

face model.

Furthermore, research in using computer

generated face models to support low bandwidth

virtual videoconferencing or video streaming is

sparse. However, quite interestingly (Liu and Wang,

2010) proposed a low bandwidth teleconferencing

system by combining techniques of facial muscle

model, face detection and facial feature extraction to

create life-like face animation. Using the symmetry

property of the face, feature points were detected.

Transformation values were then computed based on

the position of the feature points which were then

sent via a network to the receiver frame by frame.

We note here that our proposed system sends AU

numbers and their activation values rather than

transformation values. Finally (Prakash and

Balasubramanian, 2010) provide a good review of

techniques developed in the area of facial modeling

and animation while (Alkawaz et al., 2014) presents

the future directions and challenges in the facial

animation field.

3 SYSTEM OVERVIEW

Our Facial Animation System (FAS) was developed

in three parts. The first part included work done on

the facial model itself. Waters initial model was used

and enhanced by giving it an easy to use graphical

user interface (GUI) to control the functionalities of

the program. Also greater control over rendering and

animation is provided. The second part involved

work done on the expression editor. Waters original

linear muscle set used in the program is extended to

include more muscles (in particular sphincter and

sheet muscles) in order to create more interesting

facial expression. The expression editor gives user

the control over all muscles used on the face model

including jaw rotation. Using this control the user

can contract and relax muscles and can record

expressions for each FACS AUs quite easily. The

recordings for individual AUs are used by the

Remote Player which is the third component

developed. The remote player enables live video

only streaming. It opens up a TCP socket and

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228

receives streaming data from a remote source. This

streaming data is made up of series of AUs defining

individual expressions. A small animation engine is

also designed within this player. The information

about expressions (that is AUs) is processed

according to the co-occurrence rules (if chosen)

developed by (Wojdel and Rothkranz, 2005) and

then played over time creating animation on our 3D

face model. Figure 1 illustrates the architecture of

FAS.

4 FACIAL ANIMATION SYSTEM

(FAS)

This section describes the work done on our Facial

Animation System (FAS) driven by FACS in three

distinct categories:

1. Enhancements and improvements made to the

original Waters program to provide a better user

interface (UI) and greater control over rendering

options and animation. This includes inclusion of

sphincter and sheet muscles to control the facial

geometry for expression creation.

2. Work done on our expression editor that allows

the user control over all the muscles on the face

to create an expression. It gives the user the

flexibility to score expressions for individual

FACS AUs and even record them. These

expression recordings for individual AUs are

used by the Remote Player.

3. The Remote Player itself. Once a TCP

connection is established with the server

transferring in sequence batches of AU numbers

for expressions, the player runs these batches

over time creating animation.

4.1 The Reference Face Model

The original Waters face geometry is retained and

used in this project. The geometry comprises of 236

interconnected vertices forming 876 triangular

polygons. In addition to the 18 existing linear

muscles, our model introduces 2 sheet muscles and 3

sphincter muscles. The mathematics behind these

pseudo-muscles are drawn from (Tanguy, 2001) and

(Edge and Maddock, 2001) which is based on the

original Waters model. We note that the original

sphincter muscle used around the mouth area

converges vertices towards the centre of the muscle.

This makes it inappropriate to use the same

implementation of the sphincter muscle around the

eyes. To correct the problem we modified the

sphincter muscle equation so that the vertices

converge towards the semi-major axis of the ellipse

defining the influence area around the eyes. This

gives the model the ability to “squint” eyes. Figure 2

shows all the muscles implemented in FAS.

Figure 2: Muscles implemented in FAS.

4.2 Expression Editor

The Expression editor forms an integral part of our

FAS. It gives the user control over all the muscles

(including the jaw) used in our face model hence

allowing a wide range of expressions to be generated

with ease. Since our aim is to animate facial

expression based on FACS AUs, it gives the

flexibility to generate expressions that can

approximate the various AUs defined in FACS.

Using the expression editor the user can manipulate

one or more muscles to match a particular AU

expression. During this activity the calibration is

rendered in the graphics window for the user to note

the deformations on the face model. This allows the

user to fine tune the expression. Once the desired

expression is generated to a satisfactory level it can

then be recorded with the AU number as an

identifier. There is also the possibility to modify an

existing AU expression if desired by the user. A

total of 64 AU expressions can be defined.

By the definition of FACS, complex facial

expressions are generated by applying one or more

individual AUs. When these AU expressions are

programmed and stored in FAS, it gives us the

possibility to generate many other compound

expressions simply by using combinations of AUs.

The UI for our expression editor is fairly easy to use

(Figure 3).

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229

Figure 3: Expression Editor UI.

4.3 The Remote Player

Improving the Waters facial model by including the

sphincter and sheet muscles and fixing the jaw

movement has been the first major development

task. This allowed creating more interesting

expressions. Further the expression editor gives us a

novel way to blend in FACS results with our

modified facial model. This has been the second

major task. However a system that only allows

creation and recording of AU expressions will seem

of not much use. It will be interesting to see hence

how our system will behave if we present to it a

series of random AUs to animate. Surely the end

result is worth noting and whether it corresponds to

a valid human expression.

This led to the implementation of a small low-

bandwidth video streaming system, a real time facial

animation player driven by FACS AUs transmitted

as plain text over TCP sockets. The reasons for

developing this are in twofold:

1. It will allow the testing for facial expression

creation and animation by streaming AU

numbers as plaintext over the internet instead of

high-end media files.

2. Such a system would prove useful for example in

live streaming or streaming recorded lecture

presentations from across different university

campuses. Instead of clogging bandwidth with

high resolution video data to be transmitted, if

AU numbers representing facial deformation of

the person presenting the lecture can be

indentified and sent, this would significantly

reduce the usage of the bandwidth.

In general, multimedia content has a large volume,

so media storage and transmission costs are still

significant; even in today’s computing world where

bandwidths have increased significantly. To offset

this somewhat, media are generally compressed for

both storage and streaming. This however reduces

the quality. Our system attempts to solve this

problem by eliminating the need to send the actual

video of the person delivering the lecture. Instead

the user will be presented with a facial model on the

client side which will deform according to AUs sent

from the server side.

Developing media players capable of streaming

live video over the internet are not trivial. There are

three fundamental problem areas that have to be

addressed.

 Bandwidth - If the sender transmits faster than

the available bandwidth then congestion occurs,

packets are lost, and there is a severe drop in

video quality. If the sender transmits slower than

the available bandwidth then the receiver

produces sub-optimal video quality.

 Delay Jitter – This is a problem because the

receiver must receive/decode/display frames at a

constant rate, and any late frames resulting from

the delay jitter can produce problems in the

reconstructed video, e.g. jerks in the video.

 Loss Rate - Losses such as lost packets, bit errors

or burst errors can have a very destructive effect

on the reconstruction of the video on the receiver

side.

It is also desirable that playback can begin while

data is still being delivered. This requires the usage

of an appropriate buffering algorithm and the use of

an appropriate size buffer which is basically shared

by two processes – one that fills up the buffer with

video data to play and the other that actually plays it.

However unlike conventional media players, our

remote player is very much different in the sense

that it is driven only by streaming AU numbers. This

required custom algorithms to be designed for

buffering and playback sequences.

The remote player system consists of an AU

Reader, an AU Blender, an AU Mixer and a

Playback Controller. All these components are part

of FAS except the AU Reader that runs as a separate

thread from the program. The first step is to provide

all the parameters necessary to run the remote player

efficiently as illustrated in Figure 4.

Figure 4: Remote Player user interface.

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230

Figure 5: AU Reader Algorithm.

4.3.1 AU Reader

The AU Reader is a key component in the remote

player. It is responsible for receiving the transmitted

data from the server. In fact the functionality of the

AU Reader is handled by a thread that is created as

soon as the user initiates a connection. The use of a

thread is desirable because our OpenGL based FAS

process is already running in an endless loop

drawing frames every few milliseconds. What we

needed is a background process that will simply

work alongside our FAS, receiving and recording

AUs and feeding them to the playback controller.

This leads to the obvious advantage that the parent

FAS process is not overloaded to do the job of

receiving AUs from the server which if it did would

obviously create disruptions in rendering. Figure 5

describes the algorithm used in AU Reader.

AU Reader also accesses and manipulates the

two buffers – Receiver and Playout. In order for the

receiver and server to communicate with each other

efficiently the following control constants are used:

1 – successful receipt of packet. (Clients signals

server that packet received in good state, no loss). A

‘packet’ in our case is simply a series of AU

numbers (a set) that corresponds to an expression.

For example, for the expression - happiness, the

‘packet’ would simply contain the string (plaintext)

{1, 6, 12, 14}, for anger {2, 4, 7, 9, 10, 20, 26} and

{-2} for end of transmission etc.

- 1 – client signals server to pause transmission.

(The likely cause of this would be the Receiver

buffer has become full and the playback controller

is slow in freeing up Playout buffer)

- 2 – server signals client for end of transmission.

- 3 – client signals server to resume transmission.

(Receiver buffer now has space to accommodate

more AUs)

- 4 – client signals server to stop transmission. (This

is when the user decides to stop the streaming pre-

maturely)

Since the Playout buffer is also accessed by the

playback controller (a shared resource), to avoid any

deadlock situation a condition variable is defined

and used called player_buffer_state. This

variable is set to zero (0) when the Playout buffer is

or becomes empty and set to one (1) when the

Playout buffer is not 100% empty. A value of one

(1) also indicates to the AU Reader that the playback

controller is processing the current set of AUs. The

AU reader fills the Playout buffer when it finds the

state of this condition variable is zero.

4.3.2 The Playback Controller

The Playback controller works on the concept of key

frames. That is two key frames (start/end)

representing two different expressions are

determined. The in-between frames are then

interpolated resulting in animation. The end frame

becomes the start frame in the next sequence and so

on. A control variable (current_frame_state)

is used to keep track whether interpolating between

Low Bandwidth Video Streaming using FACS, Facial Expression and Animation Techniques

231

Figure 6: Playback Controller Algorithm.

Figure 7: FIFO queue implementation of Playout buffer.

start and end frame has been completed.

It is set to zero (0) if the player has finished

playing the end frame, one (1) otherwise. Each entry

in the Playout buffer represents one key frame in the

form of a set of AUs. Each set therefore describes

one expression.

Once initiated by the AU Reader the playback

controller function executes at a constant rate.

Figure 6 illustrates the complete playback controller

algorithm and the Playout buffer is implemented as

first in first out (FIFO) queue structure as illustrated

in Figure 7.

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4.3.3 AU Blender

The function of the blender is to receive a set of AUs

and apply the co-occurrence rules discussed in

(Wojdel and Rothkranz, 2005) thus resulting in a

refined set of AU which can then be animated. A

total of 16 AU expressions are implemented in our

system (Table 1). The reason for these choices of

AUs is that they are used to produce the 6 basic

expressions (happy, sad, anger, fear, surprise and

disgust). These basic expressions are implemented in

the original Water model. However they have been

produced by manipulating the muscles of the face so

that the desired expression can be achieved. Our

system tries to arrive at the same expression but by

applying individual expressions governed by

recorded AUs in the program. The AU Blender

therefore implements the same dependencies as

provided by (Wojdel and Rothkranz, 2005) but only

for the 16 AU given in Table 1.

Table 1: Action Units (AUs) implemented.

AU FACS Name Muscle Reference

Inner Brow Raiser Frontalis

Outer Brow Raiser Frontalis

Brow Lowerer

Corrugator supercilii,

Depressor supercilii

Upper Lid Raiser Levator palpebrae superioris

Cheek Raiser Orbicularis oculi

Lid Tightener Orbicularis oculi

Nose Wrinkler

Levator labii superioris

alaeque nasi

Upper Lip Raiser Levator labii superioris

Lip Corner Puller Zygomaticus major

Cheek Puffer Levator anguli oris

Dimpler Buccinator

Lip Corner Depressor Depressor anguli oris

Lower Lip Depressor Depressor labii inferioris

Chin Raiser Mentalis

Lip stretcher Risorius

Lip Tightener Orbicularis oris

Jaw Drop Masseter

4.3.4 AU Mixer

Facial expressions in a real life rarely contain only

one single AU activation (Wojdel and Rothkranz,

2005). This means meaningful facial expressions

consists of activations of more than one AU. Each

AU activation in our system is recorded as activation

values for the 18 linear muscles, 3 sphincter, 2 sheet

muscles and jaw rotation – a total of 24 values.

Some of the values are none-zero if the

corresponding muscle is activated and zero

otherwise. In order to accumulate changes resulting

from activation of multiple AUs to create an

expression, we use the following mechanism.

Average – In this mixer the muscle activation values

for each AU in the set of AUs to generate an

expression is investigated in parallel. If the

corresponding muscles are activated in the AUs,

their values are added and averaged according to the

number of AUs which have that muscle activated.

Table 2 illustrates the average mixer functionality

(only a subset of muscles is shown). It shows from

this technique that muscles that are commonly used

in many AUs, their activation values are averaged

across the number of AUs.

Maximum – Here, activation values of common

muscles across AUs are analyzed and the maximum

activation value selected. The advantage of this

mixer is that subtle changes would be overshadowed

by higher muscle activation values giving maximum

deformation. Table 3 shows the implementation of

the maximum mixer.

Table 2: Average mixer technique used in FAS.

Muscle

Muscle Activation

Average

Activation

AUa AUb AUc

Left Zygomatic

Major

1.2 0.0 0.6 0.9

Left Angular

Depressor

0.8 0.2 1.4 0.8

Left Frontalis Inner 0.0 0.0 0.9 0.9

… … … … …

Table 3: Maximum mixer technique used in FAS.

Muscle

Muscle Activation

Maximum

Activation

AUa AUb AUc

Left Zygomatic Major 1.2 0.0 0.6 1.2

Left Angular Depressor 0.8 0.2 1.4 1.4

Left Frontalis Inner 0.0 0.0 0.9 0.9

… … … … …

5 TEST RESULTS

We test the Remote Player’s ability to animate

expressions based on a given set of AUs. We use the

AU set defined for the 6 basic expressions in (Noh

and Neumann, 1998). We also test the system

Low Bandwidth Video Streaming using FACS, Facial Expression and Animation Techniques

233

combining some of these basic expressions and

evaluate their results. Table 4 describes the

application of the co-occurrence rules as the

transmitted AUs pass through the AU Blender and

AU Mixer functions. Table 5 gives results of a

simple test performed to measure the bandwidth

usage of transferring single expressions, the duration

of playback and the amount of time the remote

player spent on receiving each expression. The data

for this table was gathered by performing 5

executions cycles for each single expression.

Table 4: Application of co-occurrence rules for the six

basic human expressions.

Expression AUs Transmitted

Resultant list of AUs

after applying

co-occurrence rules

Surprise

1, 2, 5, 15, 16, 20, 26 2, 5, 11, 15, 26

Fear

1, 2, 4, 5, 15, 20, 26 2, 4, 5, 11,15, 26

Disgust

2, 4, 9, 15, 17 2, 4, 9, 11, 15, 17

Anger

2, 4, 7, 9, 10, 20, 26 2, 4, 9, 11, 15, 26

Happiness

1, 6, 12, 14 6, 11, 14, 15

Sadness

1, 4, 15, 23 4, 11, 15, 23

As an example from this analysis, it can be

calculated that approximately 33 frames of

“surprise” like expressions can be transmitted by

using only 5% of the total bandwidth with the

remaining 95% to be used for other purposes

(including audio transfer). This shows video

transferred only as a series of AU activations is

clearly an effective way to minimize bandwidth

usage and at the same time achieve animation of the

desired expression

6 CONCLUSION

This research also led to study of other areas of

research such as psychology (study of FACS) and

anatomy (study of the human face muscles aspect –

due to adoption of the Waters muscle model). Our

main research objective was to show the feasibility

of using the Facial Action Coding System (FACS)

paradigm for the generation of facial expressions.

The results obtained by our research are an

affirmation that indeed FACS can be used as a basis

for facial expression and animation.

We note that real muscles when contracted,

contract with the skin rather than just displacing

skin; have different and unequal volumes across the

muscle shape that determines how much it can be

contracted; and overlap each other where in many

cases activation of one muscle triggers the activation

of adjacent muscles. Hence future direction of this

research could be development of real 3D muscle

models governed by laws of fluid dynamics.

Table 5: Bandwidth usage for single expressions.

Bandwidth size: 100Mbps LAN Client Platform: System 1

Playout Buffer Size: 30 CPU: INTEL Core 2 Duo 2194 Mhz

Receiver Buffer Size: 100 RAM: 1GB

Player Speed: 250ms GFX: INTEL Q35 Express 384MB

Threshold: 40% AVERAGE FPS: (1024x768) 469.6

Expression

Server Side Receiver Side

Bandwidth

used

Bytes

Transferred

Transmission send

Time (average seconds)

Bytes

Received

Transmission

Recv time

(average-seconds)

Playback duration

(average-seconds)

Surprise

19 0.0189 19 0.0149 1.0212 0.148%

Fear

18 0.0228 18 0.0150 1.0307 0.141%

Disgust

13 0.0155 13 0.0148 1.0213 0.102%

Anger

18 0.0149 18 0.0155 1.0305 0.014%

Happiness

11 0.0154 11 0.0154 1.0185 0.086%

Sadness

11 0.0214 11 0.0153 1.0152 0.086%

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