Fingerprint classification and identification algorithms for criminal investigation: A survey

Win, Khin Nandar; Li, Kenli; Chen, Jianguo; Fournier-Viger, Philippe; Li, Keqin

doi:10.1016/j.future.2019.10.019

Cited by 64 publications

(31 citation statements)

References 37 publications

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“…Generally, the first phase for any large-scale fingerprint recognition system (for both software and hardware systems) [24,25] is fingerprint classification [26]. Classifying a fingerprint image is crucial and represents a very difficult morphological structure recognition problem, due to variability of inter-class and intra-class features and for the presence of noise.…”

Section: Related Workmentioning

confidence: 99%

Fingerprint Classification Based on Deep Learning Approaches: Experimental Findings and Comparisons

et al. 2021

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Biometric classification plays a key role in fingerprint characterization, especially in the identification process. In fact, reducing the number of comparisons in biometric recognition systems is essential when dealing with large-scale databases. The classification of fingerprints aims to achieve this target by splitting fingerprints into different categories. The general approach of fingerprint classification requires pre-processing techniques that are usually computationally expensive. Deep Learning is emerging as the leading field that has been successfully applied to many areas, such as image processing. This work shows the performance of pre-trained Convolutional Neural Networks (CNNs), tested on two fingerprint databases—namely, PolyU and NIST—and comparisons to other results presented in the literature in order to establish the type of classification that allows us to obtain the best performance in terms of precision and model efficiency, among approaches under examination, namely: AlexNet, GoogLeNet, and ResNet. We present the first study that extensively compares the most used CNN architectures by classifying the fingerprints into four, five, and eight classes. From the experimental results, the best performance was obtained in the classification of the PolyU database by all the tested CNN architectures due to the higher quality of its samples. To confirm the reliability of our study and the results obtained, a statistical analysis based on the McNemar test was performed.

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Section: Related Workmentioning

confidence: 99%

Fingerprint Classification Based on Deep Learning Approaches: Experimental Findings and Comparisons

et al. 2021

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“…In order to detect points of singularity from a given fingerprint image, ridge orientations are estimated [13].…”

Section: Ridge Orientation Estimationmentioning

confidence: 99%

“…e objects of interest in the foreground and background of a given image correspond to the C 1 and C 2 , respectively. Equations (13) and (14) are the respective probabilities:…”

Section: Minutiae Extraction With Sequential Binarizationmentioning

confidence: 99%

Implementation of a Transform-Minutiae Fusion-Based Model for Fingerprint Recognition

Appati

Nartey

Owusu

et al. 2021

International Journal of Mathematics and Mathematical Sciences

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Biometrics consists of scientific methods of using a person’s unique physiological or behavioral traits for electronic identification and verification. The traits for biometric identification are fingerprint, voice, face, and palm print recognition. However, this study considers fingerprint recognition for in-person identification since they are distinctive, reliable, and relatively easy to acquire. Despite the many works done, the problem of accuracy still persists which perhaps can be attributed to the varying characteristic of the acquisition devices. This study seeks to improve the issue recognition accuracy with the proposal of the fusion of a two transform and minutiae models. In this study, a transform-minutiae fusion-based model for fingerprint recognition is proposed. The first transform technique, thus wave atom transform, was used for data smoothing while the second transform, thus wavelet, was used for feature extraction. These features were added to the minutiae features for person recognition. Evaluating the proposed design on the FVC 2002 dataset showed a relatively better performance compared to existing methods with an accuracy measure of 100% as to 96.67% and 98.55% of the existing methods.

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“…Only those belonging to the same class as the input sample will be processed in the following steps. This method increases the speed of processing and allows to handle massive databases [22], [23]. Nevertheless, latent fingerprints usually correspond to partially or poorly acquired data making these preprocessing tasks almost impossible.…”

Section: Related Work: Fingerprint Identificationmentioning

confidence: 99%

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

et al. 2020

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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.

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Fingerprint classification and identification algorithms for criminal investigation: A survey

Cited by 64 publications

References 37 publications

Fingerprint Classification Based on Deep Learning Approaches: Experimental Findings and Comparisons

Fingerprint Classification Based on Deep Learning Approaches: Experimental Findings and Comparisons

Implementation of a Transform-Minutiae Fusion-Based Model for Fingerprint Recognition

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

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