Rao, A Tirupathi and C, Krishna Mohan (2016) EFFICIENT APPROACHES FOR FINGERPRINT AND PALMPRINT RECOGNITION. PhD thesis, Indian institute of technology Hyderabad.

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Fingerprint and palmprint are the commonly used biometric traits among all the biometrics. With the drastic improvement in technology, biometrics are making their way to mobile handheld devices, with limited storage and computation power. So, there is a need for recognition approaches that are efficient and optimal in resource utilization. In this thesis, we propose techniques to improve the overall efficiency and response time of fingerprint and palmprint recognition systems. To achieve this, we propose various approaches targeting the different stages in a biometric recognition system, namely, pre-processing, feature extraction, and matching. In this thesis, the following methods are proposed: 1) enhancements in pre-processing stage to improve the efficiency of extracting the minutiae points that increases the performance of cross sensors fingerprint matching, 2) efficient fingerprint matching using hybrid fingerprint matching with k-nearest neighbors and minutia quadruplet features, 3) semiautomated latent fingerprint recognition using global minutia matching technique, and 4) efficient minutiae point detection in feature extraction for palmprint recognition, and efficient minutiae quadruplet matching to improve the accuracy of high resolution full palmprint matching. The first approach in this thesis aims to improve the accuracy of minutia detection using local and global adaptive binarization in pre-processing stage. The performance of fingerprint sensors deteriorate over time which causes the appearance of noise and ghost images in the background during capture which in turn produces too many false minutiae points in feature extraction. To remove these false minutiae, we propose a local and global adaptive binarization that reduces the noise and ghost images present in the fingerprint background. A comparative study is conducted to evaluate the proposed technique across 3 optical sensors, namely, Cogent-200, BioMini-Plus, and Upek, in the presence of ghosting and noise. We demonstrate that removal of false minutiae using local and global adaptive binarization improves, the performance of global minutiae matching and NIST Bozorth matching algorithms. The existing fingerprint matching algorithms use more information in memory, due to which the matching process is time consuming. So, we propose a hybrid matching algorithm with k-nearest neighbor and minutia quadruplets for recognising plain fingerprint. The minutiae quadruplets are calculated considering very few characteristics of minutiae points to reduce the memory required for storage and subsequently the time required for matching. The space and time complexity of proposed approach are evaluated on the finger vendor competition (FVC) ongoing data set with ISO/IEC 19794-2 template matching and verified against other existing minutiae based matching algorithms. We further compare the recognition accuracy of the proposed approach with triplet based matching on the FVC 2004 and FVC ongoing data sets. Experimental studies suggest that our approach achieves comparable performance while using less memory and computation time. In forensic applications, the latent fingerprints are of poor quality and are partial prints(i.e. minimal common area between two captured prints), there by making recognition and matching a challenging task. Therefore, a fingerprint expert needs to accurately mark the minutiae points on latent prints before they can be used for identification. So, in the third approach, we present a semi-automated latent fingerprint recognition algorithm using global minutiae matching technique on the standard ISO/IEC 19794-2 templates. We demonstrate the efficacy of the proposed method on the standard NIST SD-27 fingerprint database. Palmprint recognition closely resembles fingerprint matching as the matching criteria and minutiae feature extraction methods are almost similar. As 30% of latent prints are palm prints, there is a need for high performance palmprint matching alv gorithms. Also, in regions of palmprint with high distortion, extraction of genuine minutia points is a challenge. So, we propose an efficient palmprint feature extraction and matching algorithm using nearest neighbour minutiae quadruplets, which improves the efficiency of matching by discarding false minutia points in the identification of probable matching minutiae candidates. Further, our algorithm is a full palm to full palm matching technique, which reduces the chance of missing common areas, unlike existing palmprint matching techniques that are based on segmentation. We show that the proposed method achieves better equal error rates on the FVC ongoing and THUPALMLAB data sets. Finally, we demonstrate the feasibility of using our approaches for a large-scale fingerprint authentication by evaluating them for public distribution system (PDS) using point-of-sale (PoS) devices. In the traditional PDS systems, the commodities distribution is paper based, which lacks transparency and can be easily tampered (misused). So, we propose a system that uses fingerprint based authentication to distribute the commodities. A PoS device captures a persons fingerprint and authenticates with reference fingerprints from the Aadhaar central information repository. In the proposed system, genuine beneficiaries can be identified more accurately and misuse of government subsidies can be avoided. In summary, this thesis proposes various approaches to improve fingerprint recognition by introducing adaptive binarization for efficient minutia extraction. A hybrid fingerprint matching algorithm using minutiae quadruplets and k-nearest neighbor is proposed that uses less space and time for plain fingerprint recognition. A semi-automated matching on latent fingerprint is also proposed using global minutiae matching. This thesis also proposes to use minutiae quadruplets for full palm to palm matching, there by eliminating the need for segmentation. We also demonstrate that the methods developed during the course of this thesis can be used for large-scale e-governance applications.

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IITH Creators:
IITH CreatorsORCiD
Item Type: Thesis (PhD)
Uncontrolled Keywords: fingerprint; palmprint; latent fingerprints; minutiae; triplets; quadruplets; minutiae cylinder codes; optical sensors; binarization; thinning; ridges; valleys; thenar; cross-sensor; nearest neighbour; identification; verification
Subjects: Computer science
Divisions: Department of Computer Science & Engineering
Depositing User: Team Library
Date Deposited: 12 Jul 2019 12:08
Last Modified: 17 Mar 2022 11:18
URI: http://raiith.iith.ac.in/id/eprint/5719
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