WELCOME TO SEMINARWELCOME TO SEMINAR

SEMINAR ONIMAGE QUALITY ASSESSMENT FOR FACK

BIOMETRIC DETECTION

SEMINAR ONIMAGE QUALITY ASSESSMENT FOR FACK

BIOMETRIC DETECTION

WELCOME TO SEMINARWELCOME TO SEMINAR

SEMINAR ONIMAGE QUALITY ASSESSMENT FOR FACK

BIOMETRIC DETECTION

SEMINAR ONIMAGE QUALITY ASSESSMENT FOR FACK

BIOMETRIC DETECTION

What is a biometric?What is a biometric?

A biometric is a unique, measurable characteristic of ahuman being that can be used to automatically recognizean individual or verify an individual identity . Biometricscan measure both physiological and behavioralcharacteristics.

A biometric is a unique, measurable characteristic of ahuman being that can be used to automatically recognizean individual or verify an individual identity . Biometricscan measure both physiological and behavioralcharacteristics.

What is a biometric?What is a biometric?

A biometric is a unique, measurable characteristic of ahuman being that can be used to automatically recognizean individual or verify an individual identity . Biometricscan measure both physiological and behavioralcharacteristics.

A biometric is a unique, measurable characteristic of ahuman being that can be used to automatically recognizean individual or verify an individual identity . Biometricscan measure both physiological and behavioralcharacteristics.

BIOMETRICS

BEHAVIORAL ATTRIBUTESBEHAVIORAL ATTRIBUTES

•Signature•Keystrokes

BIOMETRICS

PHYSICAL ATTRIBUTESPHYSICAL ATTRIBUTES• Fingerprint• Face• Retina• Iris• Hand and finger geometry

Fake Biometric Detection:Fake Biometric Detection:--

1) Hardware-based techniques2) Software-based techniques

Fake Biometric Detection:Fake Biometric Detection:--

1) Hardware-based techniques2) Software-based techniques

General method of fake biometric detectionGeneral method of fake biometric detectionbased on Image Quality Assessment:based on Image Quality Assessment:--General method of fake biometric detectionGeneral method of fake biometric detectionbased on Image Quality Assessment:based on Image Quality Assessment:--

Biometrics ProcessBiometrics Process

BiometricData Collection

Transmission

new biometric sample is requested.

Biometrics ProcessBiometrics Process

QualitySufficient?

Yes

Signal Processing,Feature Extraction,Representation

new biometric sample is requested.No

No

Yes

Yes

Template MatchDatabase

Generate Template

DecisionConfidence?

FACE RECOGNITIONFACE RECOGNITION Face recognition is recognizing a special face from a set of different faces. There have been a several face recognition methods,Common face recognition methods are: Geometrical Feature Matching – Based on the extraction of a set of

geometrical features having 75% recognition rate. Eigen faces method -Uses the Principal Component Analysis (PCA) to

project faces into a low dimensional space having 90.5% recognition rate. Bunch Graph Matching- Neural Networks- Uses Probabilistic Decision-Based Neural Network

(PDBNN) for face recognition having 96% recognition rate Support Vector Machines Elastic Matching Hidden Markov Models along with SVD coefficient-

Face recognition is recognizing a special face from a set of different faces. There have been a several face recognition methods,Common face recognition methods are: Geometrical Feature Matching – Based on the extraction of a set of

geometrical features having 75% recognition rate. Eigen faces method -Uses the Principal Component Analysis (PCA) to

project faces into a low dimensional space having 90.5% recognition rate. Bunch Graph Matching- Neural Networks- Uses Probabilistic Decision-Based Neural Network

(PDBNN) for face recognition having 96% recognition rate Support Vector Machines Elastic Matching Hidden Markov Models along with SVD coefficient-

FACE RECOGNITIONFACE RECOGNITION Face recognition is recognizing a special face from a set of different faces. There have been a several face recognition methods,Common face recognition methods are: Geometrical Feature Matching – Based on the extraction of a set of

geometrical features having 75% recognition rate. Eigen faces method -Uses the Principal Component Analysis (PCA) to

project faces into a low dimensional space having 90.5% recognition rate. Bunch Graph Matching- Neural Networks- Uses Probabilistic Decision-Based Neural Network

(PDBNN) for face recognition having 96% recognition rate Support Vector Machines Elastic Matching Hidden Markov Models along with SVD coefficient-

Face recognition is recognizing a special face from a set of different faces. There have been a several face recognition methods,Common face recognition methods are: Geometrical Feature Matching – Based on the extraction of a set of

geometrical features having 75% recognition rate. Eigen faces method -Uses the Principal Component Analysis (PCA) to

project faces into a low dimensional space having 90.5% recognition rate. Bunch Graph Matching- Neural Networks- Uses Probabilistic Decision-Based Neural Network

(PDBNN) for face recognition having 96% recognition rate Support Vector Machines Elastic Matching Hidden Markov Models along with SVD coefficient-

HIDDEN MARKOV MODELHIDDEN MARKOV MODEL HMMs are generally used to model one dimensional data.

Every HMM is associated with non-observable (hidden) state and an observable sequencegenerated by the hidden states individually.

The Markov process is determined by the current state with initial state distribution π and thetransition probability matrix A. We observe only the Oi (the observation sequence), which isrelated to the (hidden) states of the Markov process by the emission probability matrix B.

Using shorthand notation HMM is defined as following:

λ =(A,B,π)

WHERE

A={aij} is the state transition probability matrix,

B={bjk} is the observation symbol probability matrix,

π={π1,π2,…,πN} is the initial state distribution.

HMMs are generally used to model one dimensional data.

Every HMM is associated with non-observable (hidden) state and an observable sequencegenerated by the hidden states individually.

The Markov process is determined by the current state with initial state distribution π and thetransition probability matrix A. We observe only the Oi (the observation sequence), which isrelated to the (hidden) states of the Markov process by the emission probability matrix B.

Using shorthand notation HMM is defined as following:

λ =(A,B,π)

WHERE

A={aij} is the state transition probability matrix,

B={bjk} is the observation symbol probability matrix,

π={π1,π2,…,πN} is the initial state distribution.

A CB

HIDDEN MARKOV MODELHIDDEN MARKOV MODEL HMMs are generally used to model one dimensional data.

Every HMM is associated with non-observable (hidden) state and an observable sequencegenerated by the hidden states individually.

The Markov process is determined by the current state with initial state distribution π and thetransition probability matrix A. We observe only the Oi (the observation sequence), which isrelated to the (hidden) states of the Markov process by the emission probability matrix B.

Using shorthand notation HMM is defined as following:

λ =(A,B,π)

WHERE

A={aij} is the state transition probability matrix,

B={bjk} is the observation symbol probability matrix,

π={π1,π2,…,πN} is the initial state distribution.

HMMs are generally used to model one dimensional data.

Every HMM is associated with non-observable (hidden) state and an observable sequencegenerated by the hidden states individually.

The Markov process is determined by the current state with initial state distribution π and thetransition probability matrix A. We observe only the Oi (the observation sequence), which isrelated to the (hidden) states of the Markov process by the emission probability matrix B.

Using shorthand notation HMM is defined as following:

λ =(A,B,π)

WHERE

A={aij} is the state transition probability matrix,

B={bjk} is the observation symbol probability matrix,

π={π1,π2,…,πN} is the initial state distribution.

D

• We divide image faces into seven regions in which each is assigned to a state in a leftto right one dimensional HMM.• We divide image faces into seven regions in which each is assigned to a state in a leftto right one dimensional HMM.

SINGULAR VALUE DECOMPOSITIONSINGULAR VALUE DECOMPOSITION A singular value decomposition of a m×n matrix X is

any function of the form:

X=UWV

where U(m×m) and V(n×n) are orthogonal matrix and W isan m×n diagonal matrix of singular values

Singular values of given data matrix containinformation about the noise level, the energy, therank of the matrix, etc.

SVD provides a new way for extracting algebraicfeatures from an image.

A singular value decomposition of a m×n matrix X is

any function of the form:

X=UWV

where U(m×m) and V(n×n) are orthogonal matrix and W isan m×n diagonal matrix of singular values

Singular values of given data matrix containinformation about the noise level, the energy, therank of the matrix, etc.

SVD provides a new way for extracting algebraicfeatures from an image.

SINGULAR VALUE DECOMPOSITIONSINGULAR VALUE DECOMPOSITION A singular value decomposition of a m×n matrix X is

any function of the form:

X=UWV

where U(m×m) and V(n×n) are orthogonal matrix and W isan m×n diagonal matrix of singular values

Singular values of given data matrix containinformation about the noise level, the energy, therank of the matrix, etc.

SVD provides a new way for extracting algebraicfeatures from an image.

A singular value decomposition of a m×n matrix X is

any function of the form:

X=UWV

where U(m×m) and V(n×n) are orthogonal matrix and W isan m×n diagonal matrix of singular values

Singular values of given data matrix containinformation about the noise level, the energy, therank of the matrix, etc.

SVD provides a new way for extracting algebraicfeatures from an image.

•The image is resized to around 50% of its size. Originally theimages have 112x92 (ORL) and after resizing the images go down to56x46 or 64x64 pixels.

•The image is resized to around 50% of its size. Originally theimages have 112x92 (ORL) and after resizing the images go down to56x46 or 64x64 pixels.

Image PreprocessingImage Preprocessing The Olivetti Research Laboratory (ORL) face database contains ten different

images of each of the 40 persons.

The images are in PGM format.

The size of each image is 112x92 pixels with 256 grey levels per pixel.

The dataset is divided into two parts – one for training and one for testing.

We use 5 images from each folder for training the system and the rest 5images for testing.

Next, SVD is applied to extract features from the images and HMM to builda recognition model.

The model returns probabilities of how likely the unseen face image lookslike each one of the images used for training and the face with the highestprobability is assigned as the recognized face.

The Olivetti Research Laboratory (ORL) face database contains ten differentimages of each of the 40 persons.

The images are in PGM format.

The size of each image is 112x92 pixels with 256 grey levels per pixel.

The dataset is divided into two parts – one for training and one for testing.

We use 5 images from each folder for training the system and the rest 5images for testing.

Next, SVD is applied to extract features from the images and HMM to builda recognition model.

The model returns probabilities of how likely the unseen face image lookslike each one of the images used for training and the face with the highestprobability is assigned as the recognized face.

Image PreprocessingImage Preprocessing The Olivetti Research Laboratory (ORL) face database contains ten different

images of each of the 40 persons.

The images are in PGM format.

The size of each image is 112x92 pixels with 256 grey levels per pixel.

The dataset is divided into two parts – one for training and one for testing.

We use 5 images from each folder for training the system and the rest 5images for testing.

Next, SVD is applied to extract features from the images and HMM to builda recognition model.

The model returns probabilities of how likely the unseen face image lookslike each one of the images used for training and the face with the highestprobability is assigned as the recognized face.

The Olivetti Research Laboratory (ORL) face database contains ten differentimages of each of the 40 persons.

The images are in PGM format.

The size of each image is 112x92 pixels with 256 grey levels per pixel.

The dataset is divided into two parts – one for training and one for testing.

We use 5 images from each folder for training the system and the rest 5images for testing.

Next, SVD is applied to extract features from the images and HMM to builda recognition model.

The model returns probabilities of how likely the unseen face image lookslike each one of the images used for training and the face with the highestprobability is assigned as the recognized face.

FILTERINGFILTERING In order to compensate the flash effect and reduce the salt noise, a

nonlinear minimum order-static filter is used. Order-statistic filter is used to improve speed and recognition rate of

the system. The filter has a smoothing role and reduces the image information. A sliding window moves from left to right and top to down with steps

of size one pixel, at each situation the centered pixel is replaced byone of pixels of the window based on the type of filter.

In order to compensate the flash effect and reduce the salt noise, anonlinear minimum order-static filter is used.

Order-statistic filter is used to improve speed and recognition rate ofthe system.

The filter has a smoothing role and reduces the image information. A sliding window moves from left to right and top to down with steps

of size one pixel, at each situation the centered pixel is replaced byone of pixels of the window based on the type of filter.

Before filtering

FILTERINGFILTERING In order to compensate the flash effect and reduce the salt noise, a

nonlinear minimum order-static filter is used. Order-statistic filter is used to improve speed and recognition rate of

the system. The filter has a smoothing role and reduces the image information. A sliding window moves from left to right and top to down with steps

of size one pixel, at each situation the centered pixel is replaced byone of pixels of the window based on the type of filter.

In order to compensate the flash effect and reduce the salt noise, anonlinear minimum order-static filter is used.

Order-statistic filter is used to improve speed and recognition rate ofthe system.

The filter has a smoothing role and reduces the image information. A sliding window moves from left to right and top to down with steps

of size one pixel, at each situation the centered pixel is replaced byone of pixels of the window based on the type of filter.

After filtering

OBSERVATION SEQUENCEOBSERVATION SEQUENCE

The observation sequence is generated by dividing each face imageof width W and height H into overlapping blocks of height L andwidth W

A L×W window is slid from top to bottom on the image and createsa sequence of overlapping blocks.

The number of blocks extracted from each face image is given by:

The observation sequence is generated by dividing each face imageof width W and height H into overlapping blocks of height L andwidth W

A L×W window is slid from top to bottom on the image and createsa sequence of overlapping blocks.

The number of blocks extracted from each face image is given by:

OBSERVATION SEQUENCEOBSERVATION SEQUENCE

The observation sequence is generated by dividing each face imageof width W and height H into overlapping blocks of height L andwidth W

A L×W window is slid from top to bottom on the image and createsa sequence of overlapping blocks.

The number of blocks extracted from each face image is given by:

The observation sequence is generated by dividing each face imageof width W and height H into overlapping blocks of height L andwidth W

A L×W window is slid from top to bottom on the image and createsa sequence of overlapping blocks.

The number of blocks extracted from each face image is given by:

FEATURE SELECTIONFEATURE SELECTIONFEATURE SELECTIONFEATURE SELECTION

QuantizationQuantizationEach element is quantized into Di distinct levels. The difference

between two quantized values is:

Every element from vector C is replaced with its quantized value:

Each element is quantized into Di distinct levels. The differencebetween two quantized values is:

Every element from vector C is replaced with its quantized value:

QuantizationQuantizationEach element is quantized into Di distinct levels. The difference

between two quantized values is:

Every element from vector C is replaced with its quantized value:

Each element is quantized into Di distinct levels. The differencebetween two quantized values is:

Every element from vector C is replaced with its quantized value:

The Training Process

After representing each face image by observation vectors, they aremodeled by a seven -state HMM

HMM is trained for each person in the database using the baum-welchalgorithm

After representing each face image by observation vectors, they aremodeled by a seven -state HMM

HMM is trained for each person in the database using the baum-welchalgorithm

The Training Process

After representing each face image by observation vectors, they aremodeled by a seven -state HMM

HMM is trained for each person in the database using the baum-welchalgorithm

After representing each face image by observation vectors, they aremodeled by a seven -state HMM

HMM is trained for each person in the database using the baum-welchalgorithm

CONCLUSIONCONCLUSION

The study of the biometric systems against different types ofattacks has been a very active field of research in recent years.Thisinterest has lead to big advances in the field of security-enhancingtechnologies for biometric-based applications. However, in spite ofthis noticeable improvement, the development of efficientprotection methods against known threats has proven to be achallenging task.

The study of the biometric systems against different types ofattacks has been a very active field of research in recent years.Thisinterest has lead to big advances in the field of security-enhancingtechnologies for biometric-based applications. However, in spite ofthis noticeable improvement, the development of efficientprotection methods against known threats has proven to be achallenging task.

CONCLUSIONCONCLUSION

The study of the biometric systems against different types ofattacks has been a very active field of research in recent years.Thisinterest has lead to big advances in the field of security-enhancingtechnologies for biometric-based applications. However, in spite ofthis noticeable improvement, the development of efficientprotection methods against known threats has proven to be achallenging task.

The study of the biometric systems against different types ofattacks has been a very active field of research in recent years.Thisinterest has lead to big advances in the field of security-enhancingtechnologies for biometric-based applications. However, in spite ofthis noticeable improvement, the development of efficientprotection methods against known threats has proven to be achallenging task.

REFERENCESREFERENCES[1]. Image Quality Assessment for Fake Biometric Detection: Application to Iris, Fingerprint,and FaceRecognition By:- Javier Galbally, Sébastien Marcel, Member, IEEE, and Julian Fierrez

[2]. A new, fast and efficient HMM-based face recognition system using a 7-state HMM along with SVDcoefficients By: - H. Miar-Naimi and P. Davari

[3].Face recognition using Singular Value Decomposition and Hidden Markov ModelsPETYA DINKOVA1, PETIA GEORGIEVA2, MARIOFANNA MILANOVA

[4] S. Prabhakar, S. Pankanti, and A. K. Jain, “Biometric recognition: Security and privacy concerns,” IEEE SecurityPrivacy, vol. 1, no. 2, pp. 33–42, Mar./Apr. 2003.

[5] T. Matsumoto, “Artificial irises: Importance of vulnerability analysis,” in Proc. AWB, 2004.

[6]J. Hennebert, R. Loeffel, A. Humm, and R. Ingold, “A new forgery scenario based on regaining dynamics ofsignature,” in Proc. IAPR ICB, vol. Springer LNCS-4642. 2007, pp. 366–375.

[1]. Image Quality Assessment for Fake Biometric Detection: Application to Iris, Fingerprint,and FaceRecognition By:- Javier Galbally, Sébastien Marcel, Member, IEEE, and Julian Fierrez

[2]. A new, fast and efficient HMM-based face recognition system using a 7-state HMM along with SVDcoefficients By: - H. Miar-Naimi and P. Davari

[3].Face recognition using Singular Value Decomposition and Hidden Markov ModelsPETYA DINKOVA1, PETIA GEORGIEVA2, MARIOFANNA MILANOVA

[4] S. Prabhakar, S. Pankanti, and A. K. Jain, “Biometric recognition: Security and privacy concerns,” IEEE SecurityPrivacy, vol. 1, no. 2, pp. 33–42, Mar./Apr. 2003.

[5] T. Matsumoto, “Artificial irises: Importance of vulnerability analysis,” in Proc. AWB, 2004.

[6]J. Hennebert, R. Loeffel, A. Humm, and R. Ingold, “A new forgery scenario based on regaining dynamics ofsignature,” in Proc. IAPR ICB, vol. Springer LNCS-4642. 2007, pp. 366–375.

REFERENCESREFERENCES[1]. Image Quality Assessment for Fake Biometric Detection: Application to Iris, Fingerprint,and FaceRecognition By:- Javier Galbally, Sébastien Marcel, Member, IEEE, and Julian Fierrez

[2]. A new, fast and efficient HMM-based face recognition system using a 7-state HMM along with SVDcoefficients By: - H. Miar-Naimi and P. Davari

[3].Face recognition using Singular Value Decomposition and Hidden Markov ModelsPETYA DINKOVA1, PETIA GEORGIEVA2, MARIOFANNA MILANOVA

[4] S. Prabhakar, S. Pankanti, and A. K. Jain, “Biometric recognition: Security and privacy concerns,” IEEE SecurityPrivacy, vol. 1, no. 2, pp. 33–42, Mar./Apr. 2003.

[5] T. Matsumoto, “Artificial irises: Importance of vulnerability analysis,” in Proc. AWB, 2004.

[6]J. Hennebert, R. Loeffel, A. Humm, and R. Ingold, “A new forgery scenario based on regaining dynamics ofsignature,” in Proc. IAPR ICB, vol. Springer LNCS-4642. 2007, pp. 366–375.

[1]. Image Quality Assessment for Fake Biometric Detection: Application to Iris, Fingerprint,and FaceRecognition By:- Javier Galbally, Sébastien Marcel, Member, IEEE, and Julian Fierrez

[2]. A new, fast and efficient HMM-based face recognition system using a 7-state HMM along with SVDcoefficients By: - H. Miar-Naimi and P. Davari

[3].Face recognition using Singular Value Decomposition and Hidden Markov ModelsPETYA DINKOVA1, PETIA GEORGIEVA2, MARIOFANNA MILANOVA

[4] S. Prabhakar, S. Pankanti, and A. K. Jain, “Biometric recognition: Security and privacy concerns,” IEEE SecurityPrivacy, vol. 1, no. 2, pp. 33–42, Mar./Apr. 2003.

[5] T. Matsumoto, “Artificial irises: Importance of vulnerability analysis,” in Proc. AWB, 2004.

[6]J. Hennebert, R. Loeffel, A. Humm, and R. Ingold, “A new forgery scenario based on regaining dynamics ofsignature,” in Proc. IAPR ICB, vol. Springer LNCS-4642. 2007, pp. 366–375.

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