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Umesh Synopsis 1

Jan 03, 2016

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ABSTRACT

Overlapped Fingerprint Recognition is a complex pattern recognition problem. It is difficult to design accurate algorithms capable of separating the overlapped fingerprint, extracting salient features and matching them in a robust way. Latent fingerprints lifted from crime scenes often contain overlapping prints, which are difficult to separate and match by state-of-the-art fingerprint matchers. A few methods have been proposed to separate overlapping fingerprints to enable fingerprint matchers to successfully match the component fingerprints. These methods are limited by the accuracy of the estimated orientation field, which is not reliable for poor quality overlapping latent fingerprints. In this proposed method, we try to improve the robustness of overlapping fingerprints separation, particularly for low quality images. Our algorithm reconstructs the orientation fields of component prints by modelling fingerprint orientation fields and then correcting it using dictionary based approach. In order to facilitate this, we utilize the orientation cues of component fingerprints, which are manually marked by fingerprint examiners. This additional mark-up is acceptable in forensics, where the first priority is to improve the latent matching accuracy. The proposed orientation field estimation algorithm consists of an offline dictionary construction stage and an online orientation field estimation stage.

In the offline stage, a set of good quality fingerprints of various pattern types (arch, loop, and whorl) is manually selected and their orientation fields are used to construct a dictionary of orientation patches. In the online stage, given a fingerprint image, its orientation field is automatically estimated using model based and dictionary based approach.

The proposed method will not only work on simulated overlapping prints, but also on real overlapped latent fingerprint images. The proposed algorithm can be more effective in separating poor quality overlapping fingerprints and enhancing the matching accuracy of overlapping fingerprints.1. INTRODUCTIONFingerprint recognition or fingerprint authentication refers to the automated method of verifying a match between two human fingerprints. Fingerprints are one of many forms of biometrics used to identify an individual and verify their identity. Because of their uniqueness and consistency over time, fingerprints have been used for over a century, more recently becoming automated (i.e. a biometric) due to advancement in computing capabilities. Typically, the input image contains only a single fingerprint. However, in practice, particularly in forensics, two or more fingerprints could overlay on top of each other, resulting in an overlapped fingerprint image. Available fingerprint matchers, however, cannot accurately match overlapping fingerprints, because they assume that a fingerprint image contains only a single fingerprint and hence single orientation field. Our interest here is to develop algorithms to separate overlapping latents that will serve as a valuable tool in forensics. Note that in forensics, the matching accuracy of latents is extremely critical even if it involves some degree of manual intervention by latent examiners, including manual markup.1.1Objective of the project

The objective of this research is to find a robust solution for separating the overlapped fingerprint particularly for latent fingerprint which is difficult to separate using existing techniques. 1.2Scope of the project The proposed method will serve a valuable tool in forensics for separation of overlapped simulated fingerprint, inked fingerprint and also on latent overlapped fingerprints. 2. LITERATURE SURVEYFan et al. [3] proposed an algorithm to separate overlapped fingerprints based on image enhancement using manually marked orientation field. However, it is very tedious and time-consuming for the user to manually mark the orientation field of each component fingerprint in the overlapped fingerprint image. Geng et al. [4] proposed to use morphological component analysis to separate overlapped fingerprints. However, their experiment shows that this algorithm can only separate the component fingerprint which dominates the overlapped image. chen et al. [1] In this paper present an algorithm to separate overlapped fingerprints: The algorithm consists of three steps: 1) Initial orientation field is estimated using local Fourier analysis. 2) Relaxation labeling method is employed to label the initial orientation field into two classes. 3) Two component fingerprints are separated by enhancing the overlapped fingerprint image using Gabor filters tuned to these two component orientation fields.

Problem with approach is it does not perform very well when the singularity region of component fingerprint overlapped because the relaxation algorithm solely based on local continuity of orientation field.

shi et al. [2] propose overlap orientation field method based on constrained relaxation labeling similar to chen et al. [1]. This algorithm is differing from chen et al[1] in following aspects.1) Utilization of non-overlapped area. In this work non-overlapped area is utilized as important constraints during relaxation labeling process. 2) Mutual exclusion constraint. We treat each overlapped block as a single object rather than two ones to strictly enforce the mutual exclusion constraint, namely, two candidate orientations in an overlapped block cannot belong to the same fingerprint. 3) Order of updating label probabilities. We sequentially update label probabilities in an overlapped block in an ascending order of the distance between itself and non overlapped area. While in [1], the label probabilities in all overlapped blocks are updated in parallel. Feng et al. [10] As this paper is similar to shi et al.[2] but the difference is improved versions of the basic algorithm for two special but frequent cases: 1) The mated template fingerprint of one component fingerprint is known and2) The two component fingerprints are from the same finger.

2.1.8 zhao et al.[3] This approach is almost overcome the drawbacks of relaxation labelling technique. In this paper, instead of separating the estimated mixed orientation field, they reconstruct the orientation fields of component fingerprints via modelling orientation fields and then predicting unknown orientation fields based on a small number of manually marked orientation cues in fingerprints. This model based method significantly improves the accuracy of overlapping fingerprints separation, especially for the practical scenario of poor quality overlapped latent images. 3. PROPOSED METHODOLOGYThe proposed orientation field estimation algorithm consists of an offline dictionary construction stage and an online orientation field estimation stage illustrated in fig. 3.1.

In the offline stage, a set of good quality fingerprints of various pattern types (arch, loop, and whorl) is manually selected and their orientation fields are used to construct a dictionary of orientation patches. In the online stage, given a fingerprint image, its orientation field is automatically estimated. The figure 3.1 shows the online and offline stage in detail. SHAPE \* MERGEFORMAT

Figure.3.1 Proposed system Overview

3.1 Offline Dictionary construction

The dictionary consists of a number of orientation patches of the same size. The numbers of orientation patches, whose orientation elements are all available, are obtained by sliding a window. The greedy algorithm for dictionary construction is described below in Table 3.1:StepDescription

1The first orientation patch is added into the dictionary, which is initially empty.

2Then we test whether the next orientation patch is sufficiently different from all the orientation patches in the dictionary. If yes, it is also added into the dictionary; otherwise, the next orientation patch is tested

3Repeat step 2 until all orientation patches has been tested.

Table 3.1 Dictionary Construction.[11]

3.2 Online stage In the online stage, given a fingerprint image, its orientation field is automatically estimated, the steps for online is described in table 3.2.StepDescription

1Initial estimation. The initial orientation field is obtained using a Model based technique [9].

2Dictionary lookup. The initial orientation field is divided into overlapping patches. For each initial orientation patch, its six nearest neighbours in the dictionary are viewed as candidates for replacing the noisy initial orientation patch.

3Context-based correction. The optimal combination of candidate orientation patches is found by considering the compatibility between neighbouring orientation patches.

Table 3.2. Online Stage.

3.2.1 Initial orientation field estimation of two component fingerprint

The initial orientation field is obtained using a Model based technique [9]. The following parameters are used in algorithm to find the orientation field.

Description

Region Segmentation by manual marking singular points, orientation cues and overlapped region.

Modelling orientation field using combination model of zero pole and polynomial model. Zero pole is use to detect area near singular point and polynomial model is used to detect remaining area. The zero pole model is

We first remove the influence of singular points from the orientation field by subtracting from, and then approximate the residual orientation field with a set of basis functions.

(2)

The cosine and sine components of the doubled residual orientations can be approximated by

(3)