Latent Fingerprint Matching Using Descriptor-Based

Hough Transform

1

Guided ByJose Martin M.J

Presented ByVishakh K.VRoll no: 61

INTRODUCTION

Law enforcement agencies are used since the early 20th century

Automated Fingerprint Identification System (AFIS)

A new AFIS is introduced for latent fingerprint matching which is notcurrently existing

2

3

TYPES OF FINGERPRINTS

Fig. 1. Three types of fingerprint impressions. Rolled and plain fingerprints are also called full fingerprints. (a) Rolled; (b) plain; (c) latent.

4

LATENT FINGERPRINTS

Lifted from surfaces of objects that are inadvertently touched or handled Usually smudgy and blurred, capture only a small finger area Large nonlinear distortion due to pressure variations

Fig. 2. Latent fingerprints of three different quality levels in NIST SD27.(a) Good; (b) bad; (c) ugly.

5

MINUTIA

Most important aspect in fingerprint analysisManually marked in latents Automatically extracted from rolled fingerprints

Fig.3. Fingerprint minutiae

6

LATENT MATCHING APPROACH

Fig. 4. Overview of the proposed approach

7

LATENT MATCHING APPROACH (Cont….)

A. Feature Extraction

1) Local Minutia Descriptor

Based on minutiae

Minutia Cylinder Code (MCC) – minutia based descriptor

Records neighbourhood minutia information as 3D function

Can be concatenated as a vector

Fig.5.(a) Latent and corresponding rolled print with a mated minutiae pair indicated(b) Sections of the cylinder corresponding to the minutia indicated in the latent and in the rolled print

8

2) Orientation Field Reconstruction

Minutiae based orientation field reconstruction algorithm is used

Estimates local ridge orientation in a block

Fig. 6. Latent fingerprint in NIST SD27 and the reconstructed orientation fieldoverlaid on the latent.

9

B. Alignment (registration)

Based on minutia matching Estimation of rotational and translational parameters Ratha et al. introduced an alignment which uses Generalized Hough

Transform Most similar minutia pair is used as base for transformation

parameters Our approach uses Descriptor-based Hough Transform (DBHT)

10

Parameter computation

Let be the minutiae sets,

To get efficient and accurate alignment,

1. voting using DBHT2. use of minutia pair that previously votes for a peak

11

ALGORITHM : Descriptor-based Hough Transform

12

ALGORITHM (Cont….)

13

C. Similarity Measure

For each alignment, a matching score between two fingerprints is computed

The minutiae matching score between the two fingerprints is given by

Where,denotes the similarity between the minutia cylinder codes of the ith pair of matched minutiae

maps the spatial distance of the ith pair of matched minutiae into asimilarity score

Take two values for Ts and mean of two matching score for two threshold aretaken

14

Fig. 7 (a)–(c) shows the latent, the true mate, and the rank-1 nonmate according to large threshold, respectively. (d)–(g) shows latent minutiae that were matched to rolled print minutiae in the following cases: (d) true mate using small threshold; (e) true mate using large threshold; (f) nonmate using small threshold; and (g) nonmate using large threshold. In (d)–(g), the scores corresponding to each case are

included.

15

Given the aligned latent orientation field and the rolled orientationfield , each containing k blocks, namely and , the similarity between the two orientation fields is given by

where, is 1 if both corresponding blocks are valid, and 0otherwise.

The overall matching score is given by

where the weight is empirically set as 0.4

16

Fig. 8. (a)–(c) show minutiae and the image of (a) a latent, (b) its true mate, and (c) the highest ranked nonmate according to minutiae matching. (d) and (f) show latent minutiae and orientation field (in blue)

aligned with minutiae and orientation field of the true mate. (e) and (g) show latent minutiae and orientation field (in blue) aligned with minutiae and orientation field of the nonmate.

17

EXPERIMENTAL RESULTS

Fig. 9. Performance of COTS2, MCC SDK, and Proposed Matcher when the union of manually marked minutiae (MMM) extracted from latents and automatically extracted minutiae by COTS2 from rolled prints is input to thematchers. (a) NIST SD27; (b) WVU LFD.

18

CONCLUSIONS AND FUTURE WORK

Presented a fingerprint matching algorithm using Descriptor Based-Hough Transform

Proposed system outperforms the well known commercial matchers

Scope of developing an indexing algorithm to speed upto include a texture-based descriptor to improve thematching accuracy

19

REFERENCES

A. A. Paulino, J. Feng, and A. K. Jain, “Latent fingerprint matching

using descriptor-based Hough transform,” in Proc. Int. Joint

Conf. Biometrics,

Oct. 2011, pp. 1–7.

Paulino,Feng,Jain Latent FP Matching Using Descriptor Based

Hough Transform_IJCB11

Wikipedia

20

21

THANK YOU