A comparative study of shallow learning and deep transfer learning techniques for accurate fingerprints vitality detection

Impedovo, Donato; Dentamaro, Vincenzo; Abbattista, Giacomo; Gattulli, Vincenzo; Pirlo, Giuseppe

doi:10.1016/j.patrec.2021.07.025

Cited by 10 publications

(7 citation statements)

References 24 publications

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“…Previous works that tried to do a comparative study of transfer learning and other image processing approaches include; [4] which did a comparative study of deep transfer learning and shallow learning in accurate fingerprint detection. The deep transfer learning architectures considered were InceptionV3, NasNet, and ResNet50.…”

Section: Thiagarajan Et Al (2020) Used a Convolutional Neuralmentioning

confidence: 99%

“…While in shallow learning linear and non-linear Gaussian support vector machines were used together with the following image descriptors: Binarized statistical image features, weber local descriptor, and local phase Quantization. [4] did not compare Transfer learning against deep learning in the same environment set-up.…”

Section: Thiagarajan Et Al (2020) Used a Convolutional Neuralmentioning

confidence: 99%

“…This training involves feature extraction from the training dataset. Once the training is done the weights are then transferred and finetuned to a smaller dataset [4]. Thus, this makes it possible for the transfer learning model to leverage on previously acquired knowledge.…”

Section: Introductionmentioning

confidence: 99%

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A Comparative Study of Deep Learning and Transfer Learning in Detection of Diabetic Retinopathy

Kamiri¹,

Wambugu²,

Oirere³

2022

IJCATR

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Computer vision has gained momentum in medical imaging tasks. Deep learning and Transfer learning are some of the approaches used in computer vision. The aim of this research was to do a comparative study of deep learning and transfer learning in the detection of diabetic retinopathy. To achieve this objective, experiments were conducted that involved training four state-of-the-art neural network architectures namely; EfficientNetB0, DenseNet169, VGG16, and ResNet50. Deep learning involved training the architectures from scratch. Transfer learning involved using the architectures which are pre-trained using the ImageNet dataset and then fine-tuning them to solve the task at hand. The results show that transfer learning outperforms learning from scratch in all three models. VGG16 achieved the highest accuracy of 84.12% in transfer learning. Another notable finding is that transfer learning is able to not only achieve high accuracy with very few epochs but also starts higher than deep learning in the first epoch. This study has also demonstrated that in image processing tasks there are a lot of transferrable features since the ImageNet weights worked well in the Diabetic retinopathy detection task.

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Section: Thiagarajan Et Al (2020) Used a Convolutional Neuralmentioning

confidence: 99%

Section: Thiagarajan Et Al (2020) Used a Convolutional Neuralmentioning

confidence: 99%

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A Comparative Study of Deep Learning and Transfer Learning in Detection of Diabetic Retinopathy

Kamiri¹,

Wambugu²,

Oirere³

2022

IJCATR

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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%

“…Feature extraction uses deep learning models (convolution neural networks (CNNs)) strategy, which is employed when there is a lack of training data or resources [54]. It can be done by using a pre-trained model, such as VGG, Inception, SqueezeNet, and ResNet, which have been trained on a large dataset project [114] like ImagNet.…”

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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An Adaptive Unloading Algorithm of Computing Tasks Based on Edge Cloud Collaboration Scenario for Internet of Things

Zhang,

Xiong,

2024

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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A comparative study of shallow learning and deep transfer learning techniques for accurate fingerprints vitality detection

Cited by 10 publications

References 24 publications

A Comparative Study of Deep Learning and Transfer Learning in Detection of Diabetic Retinopathy

A Comparative Study of Deep Learning and Transfer Learning in Detection of Diabetic Retinopathy

DeepKnuckle: Deep Learning for Finger Knuckle Print Recognition

An Adaptive Unloading Algorithm of Computing Tasks Based on Edge Cloud Collaboration Scenario for Internet of Things

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