2. Biometrics
Bio = “life” metrics = “measure”
To analyse physiological and behavioural characteristics
Biometric is based on anatomic uniqueness of a person
Three ways authentication can be done:
1. Something the person knows(password)
2. Something the person has(key , special card)
3. Something the person is(fingerprint , footprint)
6. Liveness
It is a dichotomy of feature space into living and non-living
There are essentially three different ways to introduce liveness detection into
biometric systems:
1. Using extra hardware to acquire life signs
2. Using the information already captured by the system to detect life signs.
3. Using liveness information inherent to the biometric.
7. Relevance of liveness Detection
Systems requiring high
security level
Because fingerprints
cannot be changed
Accurate authentication
8. Features and patterns
Features
1. Sweat Pores
2. Ridges and valleys
3. Perspiration
Patterns
1. Arch
2. Loop
3. Whorl
9. Fingerprint Spoofing Techniques
Direct Casting
Direct Finger is used as source.
Indirect Casting
Fingerprint obtained from secondary
sources.
10. Existing Methods
Local Binary Pattern(LBP)
Pore Detection
Power Spectrum
Ridges Wavelet
Valley Wavelet
13. Overview of the proposed method
Used for distinguishing spoofed and live.
Makes use of correlation
Based on the underlying assumption of same class
Automatic Adaptation to new fingerprint images
14. Algorithms used
Partial Least Square Method
1. It is mostly used for prediction
2. Used when factors are more as compared to observed values
3. In this method used for correlation
15. Working of PLS
X = TPT + E
Y = UQ T + F
Factors Responses
Population
Figure 3: Indirect modeling
T U
factors response
16. Support Vector Machine(SVM)
1. SVM is a supervised learning method that generates ip/op
mapping function from a set of labelled training data
2. It basically calculates probability of input applied .
3. In this method it is combined along wih the classifier algorithms
like GMM,GC and QDA
19. Working
Feature Extraction phase
Features are extracted using specific descriptor like LBP and LPQ.
Correlation Phase
Correlation is performed using PLS
Here it is also used to model relation using below equation:
Lf = TPT + E
Sf = UQ T + F
Where Lf anf Sf are two matrices after feature extraction
20. SVM Phase
Probablities are calculated for live and spoof classes
Denoted by xl,xs.
Generative Classifier Phase
Here using above probabilities result is generated
