A Review of Fingerprint Feature Representations and Their Applications for Latent Fingerprint Identification: Trends and Evaluation

Valdes-Ramirez, Danilo; Medina‐Pérez, Miguel Angel; Monroy, Raúl; Loyola‐González, Octavio; Rodríguez, Jorge; Morales, Aythami; Herrera, Francisco

doi:10.1109/access.2019.2909497

Cited by 62 publications

(43 citation statements)

References 96 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There exists a large body of works in fingerprint representation. Early works analyze fingerprints considering core and delta parameters or ridge flow methods [11], [12], whereas current approaches consider minutiae [13]. Minutiae are local structures related to specific points in the discontinuities of the fingerprint ridges, such as endings and bifurcations.…”

Section: Related Work: Fingerprint Identificationmentioning

confidence: 99%

“…Minutiae are local structures related to specific points in the discontinuities of the fingerprint ridges, such as endings and bifurcations. These structures are the basis of the well-known Minutia Cylinder-Codes descriptors (MCC) [14], widely used in the recent literature because of its high accuracy at a relatively low computational cost [13], [15].…”

Section: Related Work: Fingerprint Identificationmentioning

confidence: 99%

“…• Only the works described in [14] and [10] provide the source code or program which allows researchers to replicate the results with different databases, and therefore, we can only compare ourselves against the numbers they report in their articles. • The DMC-CC version uses the Cylinder-Codes descriptors, which were shown in a recent study to be the best minutiae descriptors for identifying latent fingerprints [13].…”

Section: A Experimental Setupmentioning

confidence: 99%

See 2 more Smart Citations

Asynchronous Processing for Latent Fingerprint Identification on Heterogeneous CPU-GPU Systems

et al. 2020

Self Cite

View full text Add to dashboard Cite

Latent fingerprint identification is one of the most essential identification procedures in criminal investigations. Addressing this task is challenging as (i) it requires analyzing massive databases in reasonable periods and (ii) it is commonly solved by combining different methods with very complex datadependencies, which make fully exploiting heterogeneous CPU-GPU systems very complex. Most efforts in this context focus on improving the accuracy of the approaches and neglect reducing the processing time. Indeed, the most accurate approach was designed for one single thread. This work introduces ALFI (Asynchronous processing for Latent Fingerprint Identification), the fastest methodology for latent fingerprint identification maintaining high accuracy. ALFI fully exploits all the resources of CPU-GPU systems using asynchronous processing and fine-coarse parallelism for analyzing massive databases. Our approach reduces idle times in processing and fully exploits the inherent parallelism of comparing latent fingerprints to fingerprint impressions. We analyzed the performance of ALFI on Linux and Windows operating systems using the well-known NIST/FVC databases. Experimental results reveal that ALFI is in average 22x faster than the state-of-the-art algorithm, reaching a value of 44.7x for the best-studied case.

show abstract

Section: Related Work: Fingerprint Identificationmentioning

confidence: 99%

Section: Related Work: Fingerprint Identificationmentioning

confidence: 99%

Section: A Experimental Setupmentioning

confidence: 99%

See 1 more Smart Citation

Asynchronous Processing for Latent Fingerprint Identification on Heterogeneous CPU-GPU Systems

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…Delaunay triangulation is used in various domains, such as algorithmic geometry [ 29 ] to solve problems, or in surface reconstruction [ 30 , 31 , 32 ]. This representation has often been used for digital fingerprints [ 33 , 34 , 35 ]. This representation allows us to create a structure containing parameters describing each template, as shown in Figure 7 .…”

Section: Fingerprint Type Recognitionmentioning

confidence: 99%

Logical Attacks and Countermeasures for Fingerprint On-Card-Comparison Systems

Vibert

Bars

Charrier

et al. 2020

Sensors

View full text Add to dashboard Cite

Digital fingerprints are being used more and more to secure applications for logical and physical access control. In order to guarantee security and privacy trends, a biometric system is often implemented on a secure element to store the biometric reference template and for the matching with a probe template (on-card-comparison). In order to assess the performance and robustness against attacks of these systems, it is necessary to better understand which information could help an attacker successfully impersonate a legitimate user. The first part of the paper details a new attack based on the use of a priori information (such as the fingerprint classification, sensor type, image resolution or number of minutiae in the biometric reference) that could be exploited by an attacker. In the second part, a new countermeasure against brute force and zero effort attacks based on fingerprint classification given a minutiae template is proposed. These two contributions show how fingerprint classification could have an impact for attacks and countermeasures in embedded biometric systems. Experiments show interesting results on significant fingerprint datasets.

show abstract

“…Biometric recognition leveraging unique biological characteristics of individuals, including fingerprints [6], [7], face [8], [9], hand geometry [10], iris [11], and voice [12], has…”

Section: Introductionmentioning

confidence: 99%

User Recognition Based on Human Body Impulse Response: A Feasibility Study

et al. 2020

View full text Add to dashboard Cite

Human recognition technologies for security systems require high reliability and easy accessibility in the advent of the internet of things (IoT). While several biometric approaches have been studied for user recognition, there are demands for more convenient techniques suitable for the IoT devices. Recently, electrical frequency responses of the human body have been unveiled as one of promising biometric signals, but the pilot studies are inconclusive about the characteristics of human body as a transmission medium for electric signals. This paper provides a multi-domain analysis of human body impulse responses (HBIR) measured at the receiver when customized impulse signals are passed through the human body. We analyzed the impulse responses in the time, frequency, and wavelet domains and extracted representative feature vectors using a proposed accumulated difference metric in each domain. The classification performance was tested using the k-nearest neighbors (KNN) algorithm and the support vector machine (SVM) algorithm on 10-day data acquired from five subjects. The average classification accuracies of the simple classifier KNN for the time, frequency, and wavelet features reached 92.99%, 77.01%, and 94.55%, respectively. In addition, the kernel-based SVM slightly improved the accuracies of three features by 0.58%, 2.34%, and 0.42%, respectively. The result shows potential of the proposed approach for user recognition based on HBIR. INDEX TERMS Biosignal, human body channel, identification, impulse response, user recognition.

show abstract

A Review of Fingerprint Feature Representations and Their Applications for Latent Fingerprint Identification: Trends and Evaluation

Cited by 62 publications

References 96 publications

Asynchronous Processing for Latent Fingerprint Identification on Heterogeneous CPU-GPU Systems

Asynchronous Processing for Latent Fingerprint Identification on Heterogeneous CPU-GPU Systems

Logical Attacks and Countermeasures for Fingerprint On-Card-Comparison Systems

User Recognition Based on Human Body Impulse Response: A Feasibility Study

Contact Info

Product

Resources

About