Post on 05-Apr-2018
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Biometric Identification
Assoc. Professor Vinod ChandranSchool of Engineering Systems
QUT
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Speech, Audio, Image and VideoTechnologies
Academic staff:Prof Sridha SridharanA/Prof. Vinod Chandran, Prof. M. Moody, A/Prof. W. Boles
Postdoctoral ResearchersDr. Michael Mason - Research FellowDr. Clinton Fooks Research Fellow
Dr. David Cole - Research Fellow
Postgraduate students: 19 PhD
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Track record of Speech, Audio, Image andVideo Technologies Group
1992-20041. Graduated 18 PhD students and 12 Mastersby research students.
2. Currently supervising 19 PhD students.3. Over 200 refereed journal and conference
publications4. Working with 15 different industries and
government organisation.5. Average external funding of
$300,000/annum.
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Technology Transfer and Commercialisation
Codan Pty Ltd : Speech enhancement system for NewGeneration HF Transceiver Australia Post : Voice Controlled Parcel Sorting Telstra : Automatic Speech Quality Measurement for
Mobile Communication Systems Queensland Police: Covert Speech Enhancement and
Suspect Identification by Voice
(Name withheld) : Intelligent Multi-Microphone SpeechEnhancement System and Covert Listening PostDesign.
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Technology Transfer and Commercialisation
Motorola Australian Research Centre: Multi-Microphone Based Speech Recognition in Cars
Boeing : Digital HF radio design Harris corporation, USA: Analog Speech Encryption
Systems.
Genista Corporation, Japan: Perceived Audio QualityMeasurement: Commercial Monitors: Automatic Audio Segmentation
and Recognition for Broadcast Monitoring.
Avaya (Lucent Technology): Speech qualitymeasurement for internet telephony. Edcare: Automated English pronunciation training
system.
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Research areas we are working on Speaker Recognition Language recognition Word Spotting Speech recognition in PDAs, mobile phones and wearable computers. Speech recognition for broadcast transcription Face Recognition
Iris Recognition Palm Recognition Finger Print Recognition Gait Recognition Motion Detection
Person tracking and human activity detection Gesture and facial expression recognition Multi-modal Recognition Hand Written Signature Recognition Document recognition
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Fingerprint
Iris & Retina scan
Handwriting
Voiceprint
Facial Geometry
DNA Typing Style
Other biometricssuch as ear shapepalm print, hand-shape, vein shapehave also beenused.
Our main focus ison voice and facerecognition.
Introduction - Biometrics
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Basis for secure access
What we know Password (can forget)
What we possess Secret key on disk, card (can be stolen)
What we are Biometric
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Requirements of a goodbiometric
Universality Everyone should have it
Distinctiveness It should not be the same for two persons
Permanence It should be unchanged for reasonable
period of time Collectability It should be possible to acquire it
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For real-life applications
Good performance (accuracy, speed,resource requirements)
Acceptability (harmless, preferably non-intrusive, easy to work with)
Circumvention (robust againstimpersonation attacks and fraud)
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What is biometrics?
Automated processing (with digitalcomputers usually) of biometric data foridentifying or verifying the identity ofliving human individuals.
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Potential applications
Crew/passenger verification Secure access to premises Criminal investigation Surveillance and counter terrorist
measures
Authenticated access to servers Authenticated electronic commerce andbanking etc.
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Recognition
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Verification
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Watch List
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Performance of differentbiometrics
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Generic architectureAcquisition
Detection and pre-processing
Feature Extraction
Matching
Identification/Verification
TrackingSegmentation
Fiducial points (eg eyes)Normalization
morphable models
PCA (eigenspace)LDA (Fisherspace)
2D Fourier spectrumCorrelation filtersGabor wavelets
Bispectral integrals
Statistical classifiersStructural methods
ANNsSVMs
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Biometrics for Internet security
Encryption keys may be stored on smartcard
Biometric to access the keys
Cancellable biometrics one reservedbiometric or key, others encryptedbefore providing to third parties
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Fingerprint
Around 2% FR at 0.1% FA (FVC20022% EER best in open category)
Sensors chip and optical
Contact imaging no need for scalenormalization
Sensor cost low, around $25
Suitable for smart card implementation Susceptible to fraudulent copying
need liveness tests
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Fingerprint processing
Consist of ridges and burrows Ridge endings and bifurcations
Minutiae are important Minutiae extracted with tuned Gabor
filters and morphological opertions Minutiae points represented as a graph Graph matching after morphing for
plastic deformations of the skin
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Fingerprint usage
Already in use at some airports Large databases available with FBI and
Police in many countries
Has potential for secure internettransaction implementations (recentpapers on secret keys stored in smartcards accessed with fingerprints)
5% of the population do not have legiblefingerprints
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Face recognition
Mature commercial implementations 10% FR for 1% FA with indoor images Within class variations pose, lighting,
expression, facial hair etc. Acceptablity is quite high but standing in
front of a booth is time-consuming Many algorithms and extensive
benchmarking efforts
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Still Face Recognition : OpenIssues
Addressing the issue of recognition being toosensitive to inaccurate facial feature localization
Robustness Small and/or noisy images Images acquired years apart Outdoor acquisition:
lighting and pose
Scaling well to larger databases optimally arbitrating and combining local and
global features
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Face Recognition -Commercial Systems
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Face Recognition usage
Natural for passport, drivers licence Easier for untrained human checking of automated
result(s) Already in use at airports and other premises User cooperation not necessary can be used with
surveillance Higher computational, storage and transmission
requirements may be a hurdle to smart cardimplementation
Potential for continued authentication of internettransactions such as an online examination or anonline chess game with biometric verificationinformation embedded in packets at presenatiationlayer.
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Face Recognition -Performance Evaluations
FERET 93-97 FRVT 2000 FRVT2002
http://www.frvt.org M2VTS XM2VTS BANCA
http://www.ee.surrey.ac.uk/Research/
VSSP/xm2vtsdb Colorade State University Web Site
http://www.cs.colostate.edu/evalfacerec
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Still Face RecognitionSystems
Holistic matching methods (Classification using whole face region) Principal component analysis (PCA) Eigenface* Linear discriminant analysis (LDA) Fisherface and subspace LDA (FLDA)*
Feature-based (structural) matching methods (Structural classification usinglocal features) Pure geometry methods Graph matching methods* Gabor wavelets & image graphs
Hybrid methods (Using local features and whole face region) Eigenface & Eigenmodules Local & global feature methods Face region and components * Top 3 in FERET tests
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3-D face recognition usingdepth information
Figure shows a 3-Dreconstruction of aface using depth
information acquiredusing a stereocamera system.
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Basic premise for Super resolution
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HR Original
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Reference : Super -Resolution Optical Flow, Technical Report CMU -RI-TR-99-36,Carnige Mellon University, USA
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Background removal
Segment moving objects from a staticbackground. Person Tracking, Face detection in video
Background changes with time of day Algorithm works by clustering and
modelling background pixels
Simple background subtractionineffective, need to adapt to lightingchanges, object movements etc.
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Comparison of methods
Original Truth VAR GMM1 GMM2 NHD
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Face Recognition research atQUT
Use of 3D data and stereo images Face tracking using colour, depth as
well Super-resolved faces from surveillance
video By-passing depth estimation and
extracting depth-dependent features Hybrid 2D-3D methods and fusion
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Voice as a biometric Speakerverification
Can be quite accurate and reliable Text-dependent and Text-independent
systems Can be low-cost (microphones, sound cards) Sensitive to audio noise, acoustic channel
changes
Natural for telephone based applications Could become important with multimedia 3G
mobile services
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Voice based systems
Most are Mel-scale cepstral coefficientand Gaussian Mixture model based
NIST evaluations technology quitemature
QUT systems have been placed no. 1 insmall vocabulary (and language id)categories
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MFCC features
Windowed overlapping frames DFT each frame Log in cepstrum converts multiplicative
effects such as channel transfer function toremovable additive bias
Frequency scale warped (linear up to 1000Hz, factor of 1.1 thereafter) to correspond to
human perception. Called Mel-scale. DCT of log of spectral energies averagedover Mel-scale bands
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Speaker Modelling and ID
Speakers are modelled using GMMs (means, covariance matrix
The speaker model, k , that maximisesthe likelihood of the given test speech(or observation), X , is identified, i.e.,
where S is the registered # of speakers.)|(maxarg
1 k Sk X pS
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Feature clusters and GMMs
HOS d MFCC i
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HOS and MFCC comparisonwith noisy speech
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Handwritten Signatures
Behavioural biometric Pen tablet systems cost only
around a hundred dollars Spatial coordinates, pen
pressure and pen angles canbe captured
Dynamics are difficult toforge acceptability is high Reliability is poorer than iris,
fingerprint, voice or facebecause of large intra-classvariations
Even with relatively lowEER, savings are potentiallyhuge with credit card fraudreduction
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Potential Applications
credit card transaction verification secure access to computers secure access to databases passport and customs checks Identity checking at examinations
Identity confirmation when voting
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An example stroke and x,y,pressure and corresponding
derivatives
Sig t ifi ti
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Signature verificationcommercial products
PenOp (Peripheral Vision, New York) for access to systems Sign-On (Peripheral Vision, New York)
claims 2.5% EER, built into software instead of password
Signer Confidence (Peripheral Vision, New York) static, used for signature verification on cheques
Cadix ID-007 (verification in 1 second) CounterMatch (AEA Technology, UK) Kappa (British Technology Group, UK) ApproveIT (Silanis Technology, Canada)
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Signature Verification Benchmarks(SVC 2004) skilled forgeries
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QUT System
On line signatures X, Y, Pressure (pen angles can be used*) overlapping frames bispectral invariant phases and other features Gaussian mixture models (with some
temporal order information as in HMMs*) Language independent Handwriting sensitive *not in demo
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Performance (in house data)
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Advantages
No need to normalize and align, warp ormorph
Works with any language signature
Robust to intrapersonal variations,scaling Fast verification
Low memory requirements uncompressed data in a few KB permodel
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References1. J. L. Wayman, Digital Signal Processing in biometric identification: A review, Proc. of ICIP -2002, vol. 1, pp. 22-25, Sept,
2002.2. K. Delac and M. Grgit, A survey of biometric recognition methods, Proc. of 46th Intl. Symposium on Electronics in marine
Elmar 2004, pp. 184-193, June 2004.3. http://www.biometrics.org 4. http://www.nist.gov 5. P.J. Phillips, P. Grother, R.J. Michaels, D.M. Blackburn, E. Tabbassi and M. Bone, Face Recognition Vendor Test 2002:
Overview and Summary, NIST Technical Report, March 2003. 6. M. Yang, D.J. Kriedman and N. Ahuja, Detecting faces in images: a survey, IEEE Trans. on Pattern Analysis and Machine
Intelligence, vol. 24, no. 1, pp. 34-58, Jan. 2002.7. R. Chellappa, C.L. Wilson and S. Sirohey, Human and Machine Recognition of Faces: A Survey, Proc. of the IEEE, vol. 83,
no. 5, pp. 705-740, May 1995.
8. K.W. Bowyer, K. Chang and P. Flynn, A survey of approaches to three -dimensional face recognition, Proc. of 17th Intl.Conf. on Pattern Recognition (ICPR), vol. 1, pp. 358-361, Aug. 2004.9. D. A. Reynolds and R. C. Rose, Robust Text -Independent Speaker Identification using Gaussian Mixture Speaker Models,
IEEE Trans. On Speech and Audio Processing, vol. 3. no. 1, pp. 72-83, Jan. 1995.10. V. Chandran, D. Ning and S. Sridharan, Speaker Identification using Higher Order Spectral Phase Features and their
Effectiveness vis--vis Mel - Cepstral Features, Proc. of the International Conference on Biometric Authentication (ICBA -2004).
11. G. Gupta and A. McCabe, A Review of Dynamic Handwritten Signature Verification, Technical Report, James CookUniversity, Australia, 1997.
12. R. Plamondon, Looking at Handwriting Generation from a Velocity Control Perspective, Acta Psychologica, vol. 82, pp. 89 -101, 1993.
13. M.S. Hwang and L.H. Li, A new remote user authentication scheme using smart cards, IEEE Trans. on Consumer Electronics, vol. 46, pp. 28-30, 2000.
14. J.K. Lee, S.R. Ryu and K.Y. Yoo, Fingerprint based remote user authentication scheme using smart cards, IEE ElectronicsLetters, vol. 38, no. 12, pp. 554-555, 2002.
15. U. Uludag and A.K. Jain, Multimedia content protection via biometrics -based encryption, Proc. of Intl. Conf. on Multimediaand Expo (ICME03), vol. 3, pp. 237 -240, July 2003.
16. B.T. Tsieh, H.T. Yeh, H.M. Sun and C.T. Lin, Cryptanalysis of a Fingerprint -based Remote User Authentication SchemeUsing Smart Cards, Proc. of 37th Annual Intl. Carnahan Conf. on Security Technology, pp. 349 -350, Oct. 2003.
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