Combining Fingerprints and their Radon Transform as Input to Deep Learning for a Fingerprint Classification Task

Hamdi, Dhekra El; Elouedi, Ines; Fathallah, Abir; Nguyuen, Mai K.; Hamouda, Atef

doi:10.1109/icarcv.2018.8581072

Cited by 9 publications

(7 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the experimental part, the proposed algorithm achieved 94.7% and 96.2% for the five-class and four-class classification problems, respectively. Hamdi et al in [47] investigated the use of the conic Radon transform as a feature extractor and a Deep Learning technique to solve fingerprint classification tasks. The used Radon technique (which represents an extension of classical Radon transform over conic sections) enabled the extraction of fingerprint global characteristics, which are invariant to geometrical transformations, such as translations and rotations.…”

Section: Neural-and Cnn-based Approachesmentioning

confidence: 99%

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

et al. 2021

View full text Add to dashboard Cite

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.

show abstract

Section: Neural-and Cnn-based Approachesmentioning

confidence: 99%

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

et al. 2021

View full text Add to dashboard Cite

show abstract

“…The model was constructed based on the combination of conic radon transform (CRT) and CNN. The results showed high classification accuracy of 96.5% [40]. A patch-based model using a fully CNN with optimal threshold to detect spoof fingerprint was utilized by Park et al in 2018.…”

Section: Previous Workmentioning

confidence: 99%

Deep fingerprint classification network

2021

View full text Add to dashboard Cite

Fingerprint is one of the most well-known biometrics that has been used for personal recognition. However, faked fingerprints have become the major enemy where they threat the security of this biometric. This paper proposes an efficient deep fingerprint classification network (DFCN) model to achieve accurate performances of classifying between real and fake fingerprints. This model has extensively evaluated or examined parameters. Total of 512 images from the ATVS-FFp_DB dataset are employed. The proposed DFCN achieved high classification performance of 99.22%, where fingerprint images are successfully classified into their two categories. Moreover, comparisons with state-of-art approaches are provided.

show abstract

“…Another application of the Radon transform in this field leverages the transform's ability to create translation and scale invariant features to use as input for a Recurrent Neural Network (RNN) [27]. However, for reasons which will be demonstrated in the next section, this paper avoids the use of RNN and draws inspiration from such studies as the one in [12], where source images and their Conic Radon transforms are fed into a CNN for fingerprint classification.…”

Section: Use Of the Radon Transformmentioning

confidence: 99%

“…In previous works such as the ones in [11] and [10], the Radon transform and its derivatives were intermediate features, used to produce the final feature vector describing the action sequence, which in turn was used as input for established machine learning techniques, such as SVMs. In this work, however, much like in [12], instead of hand-crafting features based on the transform's result, a convolutional neural network will learn those features directly from the SRF s. Particularly, we opted for a VGG-based architecture [24], because of the simplicity, the ease to train and deploy and the ability to adapt to complex pattern recognition problems that this family of convolutional neural networks demonstrates. The network used in our pipeline is shown in figure 3.…”

Section: A Vgg Cnn-based Pipelinementioning

confidence: 99%

A Compact Sequence Encoding Scheme for Online Human Activity Recognition in HRI Applications

Tsatiris

Karpouzis

Kollias

2020

Proceedings of the 21st EANN (Engineering Applications of Neural Networks) 2020 Conference

View full text Add to dashboard Cite

Human activity recognition and analysis has always been one of the most active areas of pattern recognition and machine intelligence, with applications in various fields, including but not limited to exertion games, surveillance, sports analytics and healthcare. Especially in Human-Robot Interaction, human activity understanding plays a crucial role as household robotic assistants are a trend of the near future. However, state-of-the-art infrastructures that can support complex machine intelligence tasks are not always available, and may not be for the average consumer, as robotic hardware is expensive. In this paper we propose a novel action sequence encoding scheme which efficiently transforms spatio-temporal action sequences into compact representations, using Mahalanobis distance-based shape features and the Radon transform. This representation can be used as input for a lightweight convolutional neural network. Experiments show that the proposed pipeline, when based on state-of-the-art human pose estimation techniques, can provide a robust end-to-end online action recognition scheme, deployable on hardware lacking extreme computing capabilities.

show abstract

Combining Fingerprints and their Radon Transform as Input to Deep Learning for a Fingerprint Classification Task

Cited by 9 publications

References 13 publications

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

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

Deep fingerprint classification network

A Compact Sequence Encoding Scheme for Online Human Activity Recognition in HRI Applications

Contact Info

Product

Resources

About