Comparative Experimental Investigation of Deep Convolutional Neural Networks for Latent Fingerprint Pattern Classification

Noğay, Hidir

doi:10.18280/ts.380506

Cited by 4 publications

(3 citation statements)

References 36 publications

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“…Trends in AFIS research – The use of deep learning techniques using Convolutional Neural Networks (CNNs) is spreading on numerous areas of fingerprint recognition. We note its increased performance for the classification of general patterns according to the Galton-Henry main classes [ 109 ] with an accuracy of about 83% using a pre-trained Googlenet. Other researchers [ 110 ] have used CNNs for predicting finger number or the sex of the donor with good performance.…”

Section: Friction Ridge Skin and Its Individualization Processmentioning

confidence: 99%

Interpol review of fingermarks and other body impressions (2019 – 2022)

Bécue

Champod

2023

Forensic Science International: Synergy

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Section: Friction Ridge Skin and Its Individualization Processmentioning

confidence: 99%

Interpol review of fingermarks and other body impressions (2019 – 2022)

Bécue

Champod

2023

Forensic Science International: Synergy

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“…In this study, transfer learning was applied to use those models given in Table 1. The idea of this technique is keeping the layers that are responsible for feature learning (Figure 1) and then replace the last layers with new ones depending on the problem [26]. These new layers are fixed for each base model chosen.…”

Section: Figure 2 the Workflow Of The Experimentsmentioning

confidence: 99%

Investigating Transfer Learning Performances of Deep Learning Models for Classification of GPR B-Scan Images

Dikmen¹

2022

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Recent advances in deep learning models have made them the state-of-the art method for image classification. Due to this success, they have been applied to many areas, such as satellite image processing, medical image interpretation, video processing, etc. Recently, deep learning models have been utilized for processing Ground Penetrating Radar (GPR) data as well. However, studies general focus on building new Convolutional Neural Network (CNN) models instead of utilizing baseline ones. This paper investigates the usefulness of existing baseline CNN models for classifying GPR B-scan images and aims to determine how well pre-trained models perform. To that end, a real bridge deck GPR data, DECKGPRHv1.0 dataset was used to evaluate the transfer learning performances of various CNN models. Different variants of the models in terms of varying depths and number of parameters were also considered and evaluated in a comparative manner. Although it is an older model, ResNet achieved the best results with 0.998 accuracy. The experimental results showed that there is generally a direct correlation between the simplicity of the model and its success. Overall, it is concluded that near perfect results are possible by just adapting pre-trained models to the problem without fine-tuning.

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“…Hand-based authentication systems are models that recognize fingerprints [53][54][55][56][57], palm prints, hand geometry, hand form, and hand veins [58,59]. For a long time, handbased systems have been in the limelight [60].…”

Section: Introductionmentioning

confidence: 99%

DeepKnuckle: Deep Learning for Finger Knuckle Print Recognition

et al. 2022

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Biometric technology has received a lot of attention in recent years. One of the most prevalent biometric traits is the finger-knuckle print (FKP). Because the dorsal region of the finger is not exposed to surfaces, FKP would be a dependable and trustworthy biometric. We provide an FKP framework that uses the VGG-19 deep learning model to extract deep features from FKP images in this paper. The deep features are collected from the VGG-19 model’s fully connected layer 6 (F6) and fully connected layer 7 (F7). After applying multiple preprocessing steps, such as combining features from different layers and performing dimensionality reduction using principal component analysis (PCA), the extracted deep features are put to the test. The proposed system’s performance is assessed using experiments on the Delhi Finger Knuckle Dataset employing a variety of common classifiers. The best identification result was obtained when the Artificial neural network (ANN) classifier was applied to the principal components of the averaged feature vector of F6 and F7 deep features, with 95% of the data variance preserved. The findings also demonstrate the feasibility of employing these deep features in an FKP recognition system.

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Comparative Experimental Investigation of Deep Convolutional Neural Networks for Latent Fingerprint Pattern Classification

Cited by 4 publications

References 36 publications

Interpol review of fingermarks and other body impressions (2019 – 2022)

Interpol review of fingermarks and other body impressions (2019 – 2022)

Investigating Transfer Learning Performances of Deep Learning Models for Classification of GPR B-Scan Images

DeepKnuckle: Deep Learning for Finger Knuckle Print Recognition

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