968 research outputs found
Mixing Biometric Data For Generating Joint Identities and Preserving Privacy
Biometrics is the science of automatically recognizing individuals by utilizing biological traits such as fingerprints, face, iris and voice. A classical biometric system digitizes the human body and uses this digitized identity for human recognition. In this work, we introduce the concept of mixing biometrics. Mixing biometrics refers to the process of generating a new biometric image by fusing images of different fingers, different faces, or different irises. The resultant mixed image can be used directly in the feature extraction and matching stages of an existing biometric system. In this regard, we design and systematically evaluate novel methods for generating mixed images for the fingerprint, iris and face modalities. Further, we extend the concept of mixing to accommodate two distinct modalities of an individual, viz., fingerprint and iris. The utility of mixing biometrics is demonstrated in two different applications. The first application deals with the issue of generating a joint digital identity. A joint identity inherits its uniqueness from two or more individuals and can be used in scenarios such as joint bank accounts or two-man rule systems. The second application deals with the issue of biometric privacy, where the concept of mixing is used for de-identifying or obscuring biometric images and for generating cancelable biometrics. Extensive experimental analysis suggests that the concept of biometric mixing has several benefits and can be easily incorporated into existing biometric systems
Fingerprint Image-Quality Estimation and its Application to Multialgorithm Verification
Signal-quality awareness has been found to increase recognition rates and to
support decisions in multisensor environments significantly. Nevertheless,
automatic quality assessment is still an open issue. Here, we study the
orientation tensor of fingerprint images to quantify signal impairments, such
as noise, lack of structure, blur, with the help of symmetry descriptors. A
strongly reduced reference is especially favorable in biometrics, but less
information is not sufficient for the approach. This is also supported by
numerous experiments involving a simpler quality estimator, a trained method
(NFIQ), as well as the human perception of fingerprint quality on several
public databases. Furthermore, quality measurements are extensively reused to
adapt fusion parameters in a monomodal multialgorithm fingerprint recognition
environment. In this study, several trained and nontrained score-level fusion
schemes are investigated. A Bayes-based strategy for incorporating experts past
performances and current quality conditions, a novel cascaded scheme for
computational efficiency, besides simple fusion rules, is presented. The
quantitative results favor quality awareness under all aspects, boosting
recognition rates and fusing differently skilled experts efficiently as well as
effectively (by training).Comment: Published at IEEE Transactions on Information Forensics and Securit
Human Verification using Multiple Fingerprint Texture Matchers
This paper presents a multimodal biometric verification system using multiple fingerprint matchers. Theproposed verification system is based on multiple fingerprint matchers using Spatial Grey LevelDependence Method and Filterbank-based technique. The method independently extract fingerprinttexture features to generate matching scores. These individual normalized scores are combined into afinal score by the sum rule and the final score is eventually used to effect verification of a person asgenuine or an imposter. The matching scores are used in two ways: in first case equal weights are assignedto each matching scores and in second case user specific weights are used. The proposed verificationsystem has been tested on fingerprint database of FVC2002. The experimental results demonstrate that theproposed fusion strategy improves the overall accuracy of the system by reducing the total error rate of thesystem.Keywords: - Multimodal biometric System, Fingerprint verification, SGLDM, Filterbank matching, Scorelevel fusion, Sum rule
Latent Fingerprint Recognition: Fusion of Local and Global Embeddings
One of the most challenging problems in fingerprint recognition continues to
be establishing the identity of a suspect associated with partial and smudgy
fingerprints left at a crime scene (i.e., latent prints or fingermarks).
Despite the success of fixed-length embeddings for rolled and slap fingerprint
recognition, the features learned for latent fingerprint matching have mostly
been limited to local minutiae-based embeddings and have not directly leveraged
global representations for matching. In this paper, we combine global
embeddings with local embeddings for state-of-the-art latent to rolled matching
accuracy with high throughput. The combination of both local and global
representations leads to improved recognition accuracy across NIST SD 27, NIST
SD 302, MSP, MOLF DB1/DB4, and MOLF DB2/DB4 latent fingerprint datasets for
both closed-set (84.11%, 54.36%, 84.35%, 70.43%, 62.86% rank-1 retrieval rate,
respectively) and open-set (0.50, 0.74, 0.44, 0.60, 0.68 FNIR at FPIR=0.02,
respectively) identification scenarios on a gallery of 100K rolled
fingerprints. Not only do we fuse the complimentary representations, we also
use the local features to guide the global representations to focus on
discriminatory regions in two fingerprint images to be compared. This leads to
a multi-stage matching paradigm in which subsets of the retrieved candidate
lists for each probe image are passed to subsequent stages for further
processing, resulting in a considerable reduction in latency (requiring just
0.068 ms per latent to rolled comparison on a AMD EPYC 7543 32-Core Processor,
roughly 15K comparisons per second). Finally, we show the generalizability of
the fused representations for improving authentication accuracy across several
rolled, plain, and contactless fingerprint datasets
Enhanced iris recognition: Algorithms for segmentation, matching and synthesis
This thesis addresses the issues of segmentation, matching, fusion and synthesis in the context of irises and makes a four-fold contribution. The first contribution of this thesis is a post matching algorithm that observes the structure of the differences in feature templates to enhance recognition accuracy. The significance of the scheme is its robustness to inaccuracies in the iris segmentation process. Experimental results on the CASIA database indicate the efficacy of the proposed technique. The second contribution of this thesis is a novel iris segmentation scheme that employs Geodesic Active Contours to extract the iris from the surrounding structures. The proposed scheme elicits the iris texture in an iterative fashion depending upon both the local and global conditions of the image. The performance of an iris recognition algorithm on both the WVU non-ideal and CASIA iris database is observed to improve upon application of the proposed segmentation algorithm. The third contribution of this thesis is the fusion of multiple instances of the same iris and multiple iris units of the eye, i.e., the left and right iris at the match score level. Using simple sum rule, it is demonstrated that both multi-instance and multi-unit fusion of iris can lead to a significant improvement in matching accuracy. The final contribution is a technique to create a large database of digital renditions of iris images that can be used to evaluate the performance of iris recognition algorithms. This scheme is implemented in two stages. In the first stage, a Markov Random Field model is used to generate a background texture representing the global iris appearance. In the next stage a variety of iris features, viz., radial and concentric furrows, collarette and crypts, are generated and embedded in the texture field. Experimental results confirm the validity of the synthetic irises generated using this technique
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