M.Ing. (Electrical and Electronic Engineering Technology) / Automated Fingerprint Recognition Systems (AFRSs) have not been very effective so far in matching poor quality fingerprints because the challenges involved in low quality fingerprint matching are quite different from high quality fingerprint matching. The difficulty is due to three main reasons: (i) poor quality of fingerprints in terms of the clarity of ridge information due to harsh working conditions, diseases and aging, (ii) failure to acquire adequate minutiae points after segmentation and (iii) large non-linear distortion due to pressure variations which causes ridges to deform. Thus, low quality fingerprint recognition is a difficult problem which still needs more attention. This is because the accuracy of a fingerprint matching module heavily depends on the quality of the fingerprint probe image. Poor quality fingerprints lead to maximization of False Acceptance Rate (FAR) instead of True Acceptance Rate (TAR). As a result, researchers have suggested that extended features must be incorporated to improve accuracy. These features have been successfully used by Latent Print Experts (LPEs) for crime investigation purposes to increase matching accuracy for fingerprints collected from crime scenes with those stored in the national or international databases. There are three categories of fingerprint features: (i) level 1 (e.g. delta), (ii) level 2 (e.g. minutiae) and (iii) level 3 or extended features (e.g. pores). In this work, improvements have been made through fusion of minutiae and extended feature scores together with the fingerprint image quality. However, fusion algorithms designed so far are not adaptive, i.e. they assume that the effect of the quality of the image on the matching score is the same for different matchers based on different features. To test this assumption, this work adopted an algorithm from the literature that first assigns quality score to different regions of a fingerprint. Quality scores assigned to each region of the segmented fingerprint was mapped to extracted minutiae and extended features (pores). The overall quality rating of each of these were calculated as the sum of all quality scores assigned to regions. This procedure helped the designed fusion algorithm to assign more weight on highly reliable features and less weight on unreliable features. Two experiments conducted for rating minutiae and pore features that are based on this procedure, showed that quality scores for features under study do not stay constant. An adaptive weighted sum fusion algorithm was designed, implemented, tested and compared to non-adaptive algorithms, namely, simple sum and weighted sum fusion. The proposed adaptive weighted sum differs from traditional weighted sum fusion algorithm in that it uses weights assigned to each feature based on the quality map of each region of the fingerprint as opposed to the whole image. The performance of the system was tested using PlyU High Resolution Fingerprint (HRF) Database. Two performance measures were used to rate the proposed algorithm in comparison with simple sum and traditional weighted sum, namely, Area Under the Curve (AUC) and Equal Error Rate (EER). Both these performance measures showed that the algorithm proposed in this work outperforms both simple sum and traditional weighted sum fusion approaches. The proposed algorithm yields an improvement of 8% and 13.33% in EER and AUC, respectively for weighted sum fusion and 2% and 4.8% in EER and AUC, respectively for simple sum fusion.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:uj/uj:7802 |
| Date | 25 November 2013 |
| Creators | Mngenge, Ntethelelo Alex |
| Source Sets | South African National ETD Portal |
| Detected Language | English |
| Type | Thesis |
| Rights | University of Johannesburg |
Page generated in 0.0026 seconds