Fingerspelling Recognition with Support Vector Machines and Hidden Conditional Random Fields

- A comparison with Neural Networks and Hidden Markov Models -

César R. de Souza, Ednaldo B. Pizzolato and Mauro dos Santos AnjoUniversidade Federal de São Carlos (Federal University of São Carlos)

IBERAMIA 2012

Fingerspelling Recognition with Support Vector Machines

and Hidden Conditional Random Fields

Cartagena de Índias, Colombia2012

IntroductionContext, Motivation, Objectives and

the Organization of this Presentation

3

Multidisciplinary Computing and Linguistics

Ethnologue lists about 130 sign languages existent in the world (LEWIS, 2009)

ContextMotivationObjectives

Agenda

4

Two fronts

Social Aim to improve quality of life for the

deaf and increase the social inclusion

Scientific Investigation of the distinct interaction

methods, computational models and their respective challenges


Agenda

5

This paper Investigate the behavior and applicability of SVMs

and HCRFs in the recognition of specific signs from the Brazilian Sign Language

Long term Walk towards the creation of a full-fledged

recognition system for LIBRAS

This work represents a small but important step in achieving this goal


Agenda

6

Introduction

- Brazilian Sign Language

Review

Methods

Experiments

Results

Conclusion

Literature

Libras

Support Vector MachinesConditional Random Fields


Agenda

and Tools

7

The Brazilian Sign Language (LIBRAS)Structures and the manual alphabet

8

Introduction Libras Review Methods Experiments Results Conclusion

Natural languageNot mimicsNot universal It is not only “a problem of the

deaf or a language pathology” (QUADROS & KARNOPP, 2004)

LIBRASDifficultiesGrammar

9


Highly context-sensitive Same sign may have distinct meanings

Interpretation is hardeven for humans


10


Fingerspelling is onlypart of the Grammar Needed when explicitly spelling

the name of a person or a location

Subset of the full-language recognition problem


11


12


Literature Layer architectures are common Static gestures x Dynamic gestures

One of the best works on LIBRAS handles only the movement aspect of the language(Dias et al.)

13


Few studies explore SVMs But many use Neural Networks

No studies on HCRFs and LIBRAS


Static gesture classifier

P TP P A A T T O OA

Sequence classifier

PatoHMM

ANN

Example Recognition of a fingerspelled word

using a two-layered architecture

15


YANG, SCLAROFF e LEE, 2009 Multiple layers, SVMs

Elmezain, 2011 HCRF, in-air drawing recognition

16

Models and ToolsOverview of the chosen techniques

and reasons for their choice

17

Static Gesture Recognition

Neural Networks and Support Vector Machines for the detection of static signs


18

a𝑓 (𝑥) b

c

Find such that…

19


Biologically inspired McCulloch & Pitts, Rosenblatt, Rumelhart

Neural Networks

Support Vector MachinesMaximum Margin

Multiple Classes


Perceptron Hyperplane decision Linearly separable problems

Layer architecture Universal approximator

Learning is a ill-posed problem Multiple local minima, ill-conditioning

21


Strong theoretical basis Statistical Learning Theory Structural Risk Minimization (SRM)

Neural Networks


Multiple Classes

22


Risk minimization through margin maximization Capacity control through margin control Sparse solutions considering only a few support vectors

Neural Networks


Multiple Classes

Large-margin classifiers

23


Neural Networks


Multiple Classes

Problem: binary-only classifier How to generalize to multiple classes?

24


Classical approaches One-against-all One-against-one Discriminant functions

Drawbacks Only works when equiprobable Evaluation of c(c-1)/2 machines Non-guaranteed optimum results

With 27 static gestures, this would result in 351 SVM evaluations each time a new

classification is required!


25


Directed Acyclic Graphs Generalization of Decision Trees,

allowing for non-directed cycles

Require at maximum c-1 evaluations

So, for 27 static gestures, only 26 SVM evaluations are required. Only

7.4% of the original effort

Neural Networks


Multiple Classes


26


CandidatesElimination proccess

One class eliminated at a timeABCD

A lost D lost

B lostD lost A lost

C lost

B lostA lost

C lostB lost

D lostC lost

BCD

CD

BC

AB

ABC

D C B A

A x D

B x D A x C

C x D B x C A x B

A B C D

27


However, no matter the modelWe’ll have (extreme) noise due pose transitions

How can we cope with that?

28

Dynamic Gesture Recognition

Hidden Markov Models, Conditional Random Field and Hidden Conditional Random Fields for

dynamic gesture recognition.


29

bye𝑓 (𝑥) hi

hello

Find such thatgiven extremely noisy sequences of labels, estimate the word being signed.

blyrei

hil

hmeylrlwo

30


Hidden Markov Models (HMMs)

Conditional Random Fields (CRFs)

Hidden Conditional Random Fields (HCRFs)

31


𝑝 (𝑥 , 𝑦 )=∏𝑡=1

𝑇

𝑝 (𝑦 𝑡|𝑦𝑡− 1 )𝑝 (𝑥𝑡∨𝑦 𝑡)

A B




Hidden Markov Models

Joint probability model of a observation sequence and its relationship with time

32


𝑝 (𝜔 𝑖∨𝒙 )=𝑝 (𝜔𝑖 )𝑝 ( 𝒙∨𝜔 𝑖 )

∑𝑗

𝑐

𝑝 (𝒙∨𝜔 𝑗 )

Hidden Markov Models

Marginalizing over y, we achieve the observation sequence likelihood

Which can be used for classificationusing either the ML or MAP criteria

𝑝 (𝑥 )=∑𝒚∏𝑡=1

𝑇

𝑝 ( 𝑦𝑡|𝑦𝑡 −1 )𝑝 (𝑥𝑡∨𝑦𝑡)




33


Word 1

Word 2

Word n

...

𝑝 (𝒙∨ω𝟏)

ω̂=max𝜔 𝑗∈ω

𝑝 (𝒙|𝜔 𝑗 )𝑝 (𝜔 𝑗)𝑝 (𝒙∨ω𝟐)

𝑝 (𝒙∨ω𝒏)

One model for each word

34


Hidden Markov Models have found great applications in speech recognition

However, a fundamental paradigmshift recently occurred in this field




35


Probability distributions governing speech signals could not be modeled accurately, turning “Bayes decision theory inapplicable under those circumstances”

(Juang & Rabiner, 2005)




36





37


Conditional Random Fields Generalization of the Markov models

Discriminative Models Model without incorporating

Designates a family of MRFs Each new observation originates a new MRF




38


CRFLinear-chain CRFLogistic Regression

Directional modelsNaïve Bayes HMM

Disc

rimin

ative

Gene

rativ

e

Sequ

ence

Grap

hs

Infograph based on the tutorial by Sutton, C., McCallum, A., 2007

39






𝑝 (𝒚|𝒙 )= 1𝑍 (𝒙 ) ∏𝐶𝑝∈𝒞 ∏

Ψ 𝑐∈𝐶𝑝

Ψ 𝑐 (𝒙𝒄 , 𝒚 𝒄 ;𝜃)

Ψ 𝑐 (𝒙𝒄 , 𝒚 𝒄 ;𝜃𝑝)=𝑒𝑥𝑝 {∑𝑘=1

𝐾(𝑝)

𝜆𝑝𝑘 𝑓 𝑝𝑘(𝒙𝒄 ,𝒚 𝒄)}𝑍 (𝒙 )=∑

𝒚∏𝐶𝑝∈𝒞 ∏

Ψ 𝑐∈𝐶𝑝

Ψ 𝑐 (𝒙𝒄 , 𝒚𝒄 ;𝜃)

𝑝 (𝒚|𝒙 )= 1𝑍 (𝒙 )

∏𝐶𝑝∈𝒞 ∏

Ψ 𝑐∈𝐶𝑝

𝑒𝑥𝑝 {∑𝑘=1

𝐾 (𝑝)

𝜆𝑝𝑘 𝑓 𝑝𝑘(𝒙𝒄 , 𝒚𝒄)} Parameter vector which can be optimized using gradient

methods

Potential Cliques Potential Functions

Characteristic function vector

Partition function

40


𝑝 (𝑥 , 𝑦 )=¿ ∏𝑡=1

𝑇

𝑝 (𝒚|𝒙 )= 1𝑍 (𝒙 )


Ψ 𝑐∈𝐶𝑝


𝐾 (𝑝)

𝜆𝑝𝑘 𝑓 𝑝𝑘(𝒙𝒄 , 𝒚𝒄)}

𝑩𝑝 (𝑥𝑡∨𝑦𝑡)

𝑨𝑝 (𝑦𝑡|𝑦 𝑡−1 )

∏𝑡=1

𝑇𝑩(𝑥𝑡 , 𝑦𝑡)𝑨 ( 𝑦𝑡 , 𝑦𝑡−1 )





How do we initialize those models? Reaching HCRFs from a HMM

41


∏𝑡=1

𝑇 }𝑒𝑥𝑝 {∏𝑡=1𝑇

∑𝑡=1

𝑇

❑𝐥𝐧 𝑨 ( 𝑦𝑡 , 𝑦𝑡− 1)+¿ 𝐥𝐧𝑩(𝑥𝑡 , 𝑦𝑡)∑𝑡=1

𝑇

❑

∏𝑡=1

𝑇

)(∏𝑡=1𝑇

𝑝 (𝑥 , 𝑦 )=¿𝑒❑

𝑝 (𝒚|𝒙 )= 1𝑍 (𝒙 )


Ψ 𝑐∈𝐶𝑝


𝐾 (𝑝)


∏𝑡=1

𝑇𝑩(𝑥𝑡 , 𝑦𝑡)𝑨 ( 𝑦𝑡 , 𝑦𝑡−1 )∑

𝑡=1

𝑇

❑ 𝐥𝐧 𝑨 ( 𝑦𝑡 , 𝑦𝑡− 1)+𝐥𝐧𝑩(𝑥𝑡 , 𝑦𝑡)ln

¿

42


∏𝑡=1


𝑝 (𝑥 , 𝑦 )=¿

𝑝 (𝒚|𝒙 )= 1𝑍 (𝒙 )


Ψ 𝑐∈𝐶𝑝


𝐾 (𝑝)


∑𝑡=1

𝑇


𝑇

❑

ln 𝑨

𝑛 𝑛

𝑛 𝑚

ln 𝑩a11 a12 a13

a21 a22 a23

a31 a32 a33

b11 b12

b21 b22

b31 b32

𝝀 :𝑘=𝑛×𝑛+𝑛×𝑚

43


∏𝑡=1


𝑝 (𝑥 , 𝑦 )=¿

𝑝 (𝒚|𝒙 )= 1𝑍 (𝒙 )


Ψ 𝑐∈𝐶𝑝


𝐾 (𝑝)


∑𝑡=1

𝑇


𝑇

❑

a11 a12 a13 a21 a22 a23 a31 a32 a33 b11 b12 b21 b22 b31 b32

𝒇 𝒆𝒅𝒈𝒆❑ (𝒚 𝒕 , 𝒚 𝒕−𝟏 ,𝒙 ; 𝑖 , 𝑗 )=𝟏{𝒚𝒕=𝐢 }𝟏{𝒚 𝒕 −𝟏= 𝐣} 𝒇 𝒏𝒐𝒅𝒆

❑ (𝒚 𝒕 , 𝒚 𝒕−𝟏 , 𝒙 ; 𝑖 ,𝑜 )=𝟏{𝒚𝒕=𝐢 }𝟏{𝒙 𝒕=𝐨 }

𝑘=𝑛×𝑛+𝑛×𝑚𝝀 :

i=1

j=1

i=1

j=2

i=1

j=3

i=2

j=1

i=2

j=2

i=2

j=3

i=3

j=1

i=3

j=2

i=3

j=1

i=1

o=1

i=1

o=2

i=2

o=1

i=2

o=2

i=3

o=1

i=3

o=2

𝒇 :

44


{∑❑❑

❑

∑❑

❑

❑

𝑝 (𝑥 , 𝑦 )=¿

𝑝 (𝒚|𝒙 )= 1𝑍 (𝒙 )


Ψ 𝑐∈𝐶𝑝


𝐾 (𝑝)


𝑒𝑥𝑝 {∑𝑡=1𝑇

∑𝑖=1

𝑛

∑𝑗=1

𝑛

𝑎𝑖𝑗1{𝑦𝑡=𝑖}1{𝑦𝑡− 1= 𝑗 }+∑𝑡=1

𝑇

∑𝑖=1

𝑛

∑𝑗=1

𝑚

𝑏𝑖𝑗1{𝑦 𝑡=𝑖}1{𝑥𝑡= 𝑗}}


𝐾

𝜆𝑘 𝑓 𝑘 (𝒙 , 𝒚 )}

𝒇 𝒆𝒅𝒈𝒆❑ (𝒚 𝒕 , 𝒚 𝒕−𝟏 ,𝒙 ; 𝑖 , 𝑗 )=𝟏{𝒚𝒕=𝐢 }𝟏{𝒚 𝒕 −𝟏= 𝐣} 𝒇 𝒏𝒐𝒅𝒆

❑ (𝒚 𝒕 , 𝒚 𝒕−𝟏 , 𝒙 ; 𝑖 ,𝑜 )=𝟏{𝒚𝒕=𝐢 }𝟏{𝒙 𝒕=𝐨 }¿𝑒𝑥𝑝 {∑𝑡=1𝑇

𝑎𝑖𝑗 𝒇 𝒊𝒋 (𝒚 𝒕 , 𝒚 𝒕−𝟏 , 𝒙)+∑𝑡=1

𝑇

𝑏𝑖𝑗 𝒇 𝒊𝒋 (𝒚 𝒕 , 𝒚 𝒕−𝟏 ,𝒙 )}

¿

45


𝑝 (𝑥 , 𝑦 )=¿

𝑝 (𝒚|𝒙 )= 1𝑍 (𝒙 )


Ψ 𝑐∈𝐶𝑝


𝐾 (𝑝)



𝐾

𝜆𝑘 𝑓 𝑘 (𝒙 , 𝒚 )}𝑝 ( 𝑦 )

𝑝 (𝒚|𝒙 )=¿1

𝑍 (𝒙 )

46


Drawback Assumes both and are known




47





48





49


Hidden Conditional Random Fields Generalization of the hidden Markov classifiers

𝜃𝐻𝐶𝑅𝐹

𝜃𝐻𝑀𝑀𝑐

Parameter space




50


Sequence classification Model without explicitly modeling

Do not require to be known The sequence of states is now hidden





51


∑𝑘=1

𝐾 (𝑝)

❑ ;𝜽𝒑 )}

1𝑍 (𝒙 ) ∏𝐶𝑝∈𝒞 ∏

Ψ 𝑐∈𝐶𝑝

Ψ 𝑐 ¿¿¿¿ 𝒙 )=¿∑𝒚

❑ ;𝜽𝒑 ¿,𝜔𝑐

Ψ 𝑐 (𝒙𝒄 , 𝒚𝒄𝜽𝒑;𝜽𝒑 )=𝑒𝑥𝑝 {∑𝑘=1

𝐾 (𝑝)

𝜆𝑝𝑘 𝑓 𝑝𝑘 (𝒙𝒄 ,𝒚 𝒄 ,𝜽𝒑,𝜔𝑐,𝜔𝑐

𝑝 (𝜔|𝒙 )=∑𝒚

❑𝑝 (𝒚,𝜔Hidden Markov Models (HMMs)




52


xt-2 xt-1 xt

yt-1yt-2 yt

ω

ω̂=max𝜔 𝑗∈ ω

𝑝 (𝜔 𝑗 | 𝒙)

Single model for all words

53

Experiments and ResultsFingerspelling recognition with SVMs and HCRFs against ANNs and HMMs



P TP P A A T T O OA

Sequence classifier

PatoHMM

ANN

xt-2 xt-1 xt

yt-1yt-2 yt

ω

HCRF

SVM

55


Static gesture recognition Database of 8100 grayscale images Input instances with 1024 features 27 classes (manual alphabet signs)

Static gestures(hand postures)

Dynamic gestures (spelled words)

56


Neural Networks Evaluate initialization heuristics (Nguyen-Widrow)

Support Vector Machines Evaluate the heuristic value for based on the inter-

quartile range of the norm statistics for the input dataset



57


Resilient Backpropagation ANN

Hidden Neurons Kappa

50 0.851

100 0.887

300 0.921

500 0.922

1000 0.924

Gaussian kernel SVM

Kappa

0,1 0.000

1 0.106

10 0.569

100 0.950

(heuristic) 392 0.917

1000 0.863

Static sign classification

0.1 1 10 100 10000

0.2

0.4

0.6

0.8

1

0.00100.00200.00300.00400.00500.00600.00700.00

Busca em grade Heurística Vetores de Suporte

Kapp

a

Supp

ort V

ecto

rs (A

vera

ge)

58


Resilient Backpropagation ANN

Hidden Neurons Kappa

50 0.851

100 0.887

300 0.921

500 0.922

1000 0.925

Gaussian kernel SVM

Kappa

0.1 0.000

1 0.106

10 0.569

100 0.959

(heuristic) 392 0.917

1000 0.863

0.1 1 10 100 10000

0.2

0.4

0.6

0.8

1

0.00100.00200.00300.00400.00500.00600.00700.00

Busca em grade Heurística Vetores de Suporte

Kapp

a

Supp

ort V

ecto

rs (A

vera

ge)

Static sign classification

59


0.1 1075

200 H1000

2000

0.80

0.85

0.90

0.95

1.00

11000

1000000

Hyperparameter surface for Gaussian SVMs

1 10 100 1000 10000 100000 1000000 10000000

Sigma (σ²)

Kapp

a

C

0.1 1075

200 H1000

2000

0100200300400500600

11000

1000000

1 10 100Sigma (σ²)Av

erag

e nu

mbe

r of S

Vs

C

60


Static gesture classification Statistically significant results (p < 0.01)

Points of interest Polynomial machines have increased sparcity but smaller kappa Neural networks were faster to evaluate, but not to learn – unless using linear SVMs Sigma plays a much more important role than C in Gaussian machines Heuristics for choosing sigma and C resulted in great performance values



61




62






P TP P A A T TA

Sequence classifier

Pato

xt-2 xt-1 xt

yt-1yt-2 yt

ω

HCRF

SVM

64


Dynamic gesture classification Database containing 540 signed words Containing a total of 63,703 static signs

The previous layer labels the entire dataset Then we tested all possible model combinations Estimated kappa sampled from 10-fold CV

65


Labeller Classification AlgorithmTraining Validation

Kappa Kappa

SVM HMM Baum-Welch

SVM HCRF RProp

ANN HMM Baum-Welch

ANN HCRF RProp

Dynamic gesture classification

66



Kappa Kappa

SVM HMM Baum-Welch 0.95 0.82

SVM HCRF RProp 0.98 0.83

ANN HMM Baum-Welch 0.95 0.80

ANN HCRF RProp 0.99 0.82


67


SVM+HCRF have shown the best validation result (10-fold CV) Combinations using HCRF have shown best results in general

Training results are statistically different We have not enough evidence to say validation results are not equivalent


Kappa Kappa

SVM HMM Baum-Welch 0.95 0.82

SVM HCRF RProp 0.98 0.83

ANN HMM Baum-Welch 0.95 0.80

ANN HCRF RProp 0.99 0.82


68


Hidden Conditional Random Fields – in this specific problem – had higher ability to retain knowledge while keeping the same generality

In other words, achieved less overfitting.


Kappa Kappa

SVM HMM Baum-Welch 0,95 0,82

SVM HCRF RProp 0,98 0,83

ANN HMM Baum-Welch 0,95 0,80

ANN HCRF RProp 0,99 0,82


69

Conclusionand future works


Fingerspelling experiments SVMs & HCRFs vs ANNs & HMMs

Static Gesture Recognition Statistically significant results favoring SVMs Linear SVMs on DDAGs:

Best compromise between speed, accuracy and ease of use SVMs have shown easier training, reduced training times Heuristic initializations work rather well, less parameter tuning

Dynamic Gesture Recognition Choice of gesture classifier had much more impact Linear-chain HCRFs:

Increased knowledge absorption without overfitting

71


Future works

Detect standard words rather than fingerspelling (already complete)

Use Structural Support Vector Machines, which are equivalent to HCRFs but are trained using a hinge loss function

Use a mixed language model to categorize full phrases

References

73


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Acknowledgements

Guilherme Cartacho

77

A Framework for Research Support

Appendix A

78Accord.NET Framework Machine Learning and Artificial Intelligence

79Accord.NET Framework Machine Learning and Artificial Intelligence Computer Vision / Audition

80Accord.NET Framework Machine Learning and Artificial Intelligence Computer Vision / Audition Mathematics and Statistics

Builds upon well established foundations

83

It has been used toRecognize gestures using Wii

84

It has been used to Study and evaluate performance in 3D gesture recognition

85

It has been used toPredict attacks in computer networks

86

It has been used toCompare touch and in-air gestures using Kinect

87

It has been used toProvide sensor information in multi-model interfaces

Guido Soetens, Estimating the limitations of single-handed multi-touch input. Master Thesis, Utrecht University. September, 2012.

K. N. Pushpalatha, A. K. Gautham, D. R. Shashikumar, K. B. ShivaKumar. Iris Recognition System with Frequency Domain Features optimized with PCA and SVM Classifier, IJCSI International Journal of Computer Science Issues, Vol. 9, Issue 5, No 1, September 2012.

Arnaud Ogier, Thierry Dorval. HCS-Analyzer: Open source software for High-Content Screening data correction and analysis. Bioinformatics. First published online May 13, 2012.

It has been used inan increasing number of publications

http://code.google.com/p/accord/issues/detail?id=5

http://code.google.com/p/accord/issues/detail?id=5

Ludovico Buffon, Evelina Lamma, Fabrizio Riguzzi, and Davide Forment. Un sistema di vision inspection basato su reti neurali. In Popularize Artificial Intelligence. Proceedings of the AI*IA Workshop and Prize for Celebrating 100th Anniversary of Alan Turing's Birth (PAI 2012), Rome, Italy, June 15, 2012, number 860 in CEUR Workshop Proceedings, pages 1-6, Aachen, Germany, 2012.

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Fingerspelling Recognition with Support Vector Machines and Hidden Conditional Random Fields

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