Application : Surveillance in trains

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Juli 2010 eNTERFACE Application : Surveillance in trains Video, Audio processing Sound localization, pattern rec.

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Application : Surveillance in trains. Video, Audio processing Sound localization, pattern rec. Model based approach. Automatic recognition of facial expressions and lipreading using vector flow. Lip reading. Facial expression recognition. What makes visual speech recognition so hard?. - PowerPoint PPT Presentation

Transcript of Application : Surveillance in trains

Page 1: Application : Surveillance in trains

Juli 2010 eNTERFACE

Application : Surveillance in trains

Video, Audio processingSound localization, pattern rec.

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Lip reading

Facial expression recognition

Automatic recognition of facial expressions and lipreading using vector flow

Model based approach

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What makes visual speech recognition so hard?

Visemes Smaller word separability Speech info in audio > Speech info in

video

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Lip-reading by Humans

People recognize speech better when the signal is both auditory and visual

The difference inrecognition ratesgrows with thelevel of noise inthe environment

0102030405060708090

100

noisy clear

S/N (dB)% c

orr

ect

resp

on

ses

A A+V

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Inspiration

In the 1968 Stanley Kubrick film 2001: A space odyssey the computer reads from the lip-movements the conversation of two astronauts.

Thirty years later automated lip-reading becomes a significant part of research in speech recognition systems.

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New speech corpusAV speech

corpus

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Databases of different quality and resolution

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Recording a new speech corpusAV speech

corpus

Visemes|Corpus|Tracking|Features

Applications|Problem|ASR|VSR|Training|Analysis|Conclusion|Recommendations

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Recording a new speech corpusAV speech

corpus

Visemes|Corpus|Tracking|Features

Applications|Problem|ASR|VSR|Training|Analysis|Conclusion|Recommendations

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New speech corpus Dutch Recorded at high-speed: 100 fps Front and profile views included 70 people

49 male, 21 female Students, professors,

secretaries, friends Utterances:

Sentences, digits, spelling, conversation starters/endings, open questions

Normal, fast, whispering

AV speechcorpus

Visemes|Corpus|Tracking|Features

Applications|Problem|ASR|VSR|Training|Analysis|Conclusion|Recommendations

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New speech corpusAV speech

corpus

Visemes|Corpus|Tracking|Features

Applications|Problem|ASR|VSR|Training|Analysis|Conclusion|Recommendations

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Lip-reading by Humans

People recognize speech better when the signal is both auditory and visual

The difference inrecognition ratesgrows with thelevel of noise inthe environment

0102030405060708090

100

noisy clear

S/N (dB)% c

orr

ect

resp

on

ses

A A+V

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ISFER WorkbenchExamples (continued)

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Active Contours Internal and external

energies Internal energy forces

contour to shrink Locally defined

external energy forces the contour to stop at the edge of the mouth

Computationally cheap Sensitivity to initial

setting of the contour7

9

810

12

13 13 1113

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10 9 7

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Template Matching Internal and external

energies Internal energy forces template

to maintain geometry Globally defined external

energy forces appropriate placement on the picture

Better results than with snakes Integration of energy functions at each step

can be very time consuming

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Model

Goal: lip-reading Needed:

accurate description of visible parts of articulatory system

Accurate description of the shape of the mouth: measurements of the distance of the lip to a

center of the mouth measurements of thickness of visible part of

the lips

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Data processingFiltered image

- intensity distribution- center of mouth

Image in polar coordinates

Conditional distribution

Mean and variance functions

(continued)

yxI , EYEX ,

cos,sin,ˆ rEYrEXIrI

rGaussrI m ,,ˆ

mM V

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Data visualization

Single frame

data vector:

181181 , mm

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Results of Experiments

Feed Forward BP

Vanmiddag komt de pianostemmer langs om mijn vleugel te stemmen

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Tracking the face – Optical flow

Capturing apparent motion of subsequent images in a grid of motion vectors

Advantages No lip model required Good at capturing motion

Disadvantage Slow

Face tracking

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Tracking the face – Lip Geometry Estimation

Applying some color filters and capturing the lip contours in polar coordinates

Advantages No lip model required More or less person-independent

Disadvantage Not robust to external factors

Face tracking

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Tracking the face – Active Appearance Models Point tracking according to a statistical lip

model

Disadvantage Requires annotated training images

Advantages Robust against external factors Fast!

Face tracking

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Active Appearance Models – Design of the lip model

Face tracking

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AAM model point coordinatesFace

tracking

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Features plotted for“F”

Feature extraction

time (frames)

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5-states HMM

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Automatic bi-modal human emotion recognition

Automatic recognition of facial expressions using active Appearance model

Model based approach

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Face localization

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User-interface prototype iCat tohelp users in daily tasks.

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M.A.E.L.I.A. Our digital cat

H.C.I. Group

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Juli 2010 eNTERFACEH.C.I. Group

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H.C.I. Group

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Requirements in other words…Are you out of your mind? I am sleeping!!!

Get a life! I am still sleeping!

I am so bored! I

wish I had a companion!

7:00 AM 8:00 AM

11:00 AM 14:00 AM

I feel so lonely!!! I am very sad and depressed.

16:00 AM

Finally I have a friend! I am so happy and I even managed to pick up the bone! Wow!!!

AIBO! Bring me my

newspaper!!!

AIBO! Let’s play!!! Follow

me

AIBO! Let’s play!!! Follow

me

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Multimodal Communication

Uh, ….

I have no time to do anything with you

Hello,

do you like to chat with me ?

Uh, what a nerd

I want a date

She looks nice

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Multi-modal interaction

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Would you like to join mefor a dinner ?

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Chat-session A cup of tea? Mmh, njeh, I don’t like tea. What’s wrong with tea? Tea makes me sick. That’s nonsense!! And my sister doesn’t like you too! She is very disappointed!! Hihi, I was joking!!! Oh, that’s funny!!!

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Chat-session (f) A cup of tea? : - ) (m) Mmh, njeh, I don’t like tea. (: - (

(f) What’s wrong with tea? : - o (m) Tea makes me sick. % - \

(f) That’s nonsense!! : - l l (f) My sister doesn’t like you too! : - l l (f) She is very disappointed!! : - ( (m) Hihi, I was joking!!! ; - ) (f) Oh, that’s funny!!! : - ]

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A cup of tea?

: - )

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Mmh, njeh, I don’t like tea.

(: - (

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What’s wrong with tea?

: - o

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Tea makes me sick.

% - \

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That’s nonsense!!

: - l l

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My sister doesn’t like you too!

: - l l

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She is very disappointed!!

: - (

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Hihi, I was joking!!!

; - )

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Oh, that’s funny!!!

: - ]