This document proposes a correlation based fingerprint liveness detection method using partial least squares and support vector machines. It discusses existing liveness detection techniques like local binary pattern and pore detection, as well as the need for liveness detection in fingerprint authentication systems. The proposed method works by extracting features from fingerprints, performing correlation analysis using partial least squares, calculating live and spoof probabilities using support vector machines, and classifying the fingerprints using generative classifiers. It is shown to achieve better cross-sensor performance and lower error rates than existing liveness detection methods.

1. Correlation Based Fingerprint
Liveness Detection
Under The Guidance of: Ashish Pawar
Prof. Mrs. Vidya Patil 30349

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)

3. Biometric Identifiers
 Fingerprint Recognition
 Iris Recognition
 Face Detection
 Voice Recognition
 DNA

4. Why Fingerprint?
 Unique
 Reliable
 Fast and easy capture

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

11. Comparison between existing methods
The above table gives the FerrFake rate

12. Comparison between existing methods
The above table gives the FerrLive rate

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

18. FrameWork Architecture

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

21. Comaprison with existing Methods
Analysis on livedet 2011

22. Analysis on livedet2013

23. Advantage
 Cross – sensor Interoperability
 Less Error Rate as compared to existing methods

24. Thank You!