Multi-task CNN for restoring corrupted fingerprint images

Wong, Wei Jing; Lai, Shang-Hong

doi:10.1016/j.patcog.2020.107203

Cited by 51 publications

(19 citation statements)

References 22 publications

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“…The elements are the basis functions, orthogonal on the [0,1]. Let dilation by parameter , translation by parameter and transform , by substitute parameters a, b and transform x in (1), then will be get (2). DCHWT include four parameters, k is:…”

Section: Discrete Chebyshev Wavelet Transform (Dchwt)mentioning

confidence: 99%

“…There are many methods of identifying people and identifying them, such as by fingerprint, eye print, facial recognition, and its parts. The fingerprint technology emerged to identify the person because the fingerprint of a person belongs to the person alone because it is not repeated by another person [1] but this technique may be unclear, here the ability of deep learning appears, especially the convolutional neural network system because the network convolutional neural network (CNN) is multilayered, resulting in organically extruded contrast [2]. An inquiry appears as to which deep learning strategies are ideal Ismael and Irina [3].…”

Section: Introductionmentioning

confidence: 99%

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Face recognition using enhancement discrete wavelet transform based on MATLAB

Abdulrahman¹,

Tahir

2021

IJEECS

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<p>In this work, it was proposed to compress the color image after de-noise by proposing a coding for the discrete transport of new wavelets called discrete chebysheve wavelet transduction (DCHWT) and linking it to a neural network that relies on the convolutional neural network to compress the color image. The aim of this work is to find an effective method for face recognition, which is to raise the noise and compress the image in convolutional neural networks to remove the noise that caused the image while it was being transmitted in the communication network. The work results of the algorithm were calculated by calculating the peak signal to noise ratio (PSNR), mean square error (MSE), compression ratio (CR) and bit-per-pixel (BPP) of the compressed image after a color image (256×256) was entered to demonstrate the quality and efficiency of the proposed algorithm in this work. The result obtained by using a convolutional neural network with new wavelets is to provide a better CR with the ratio of PSNR to be a high value that increases the high-quality ratio of the compressed image to be ready for face recognition.</p>

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Section: Discrete Chebyshev Wavelet Transform (Dchwt)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Face recognition using enhancement discrete wavelet transform based on MATLAB

Abdulrahman¹,

Tahir

2021

IJEECS

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show abstract

“…The authors argued that this will remain the active direction for the fingerprint research in the future. Fingerprint biometric is believed to be the fastest, most common, and oldest method for physiological biometric systems and very well acknowledged by the legal community [20][21][22]. More so, the implementation of fingerprint authentication Journal of Artificial Intelligence and Systems system is easy, cheap, convenient and with a satisfactory level of accuracy [23][24][25].…”

Section: Open Accessmentioning

confidence: 99%

“…AFRS was proposed to digitize the whole process of fingerprint acquisition and automation of the fingerprint matching techniques incorporating them in an end-to-end system to make the identification and matching process of fingerprint automatic [21,23]. A typical AFRS consists of five fundamental modules (see Figure 7) including image acquisition, preprocessing (Fingerprint Segmentation, image enhancement) feature extraction, fingerprint matching and classification [20,21].…”

Section: Fingerprint Biometricsmentioning

confidence: 99%

Deep Learning Algorithms based Fingerprint Authentication: Systematic Literature Review

Chiroma¹

2021

AIS

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Deep Learning algorithms (DL) have been applied in different domains such as computer vision, image detection, robotics and speech processing, in most cases, DL demonstrated better performance than the conventional machine learning algorithms (shallow algorithms). The artificial intelligence research community has leveraged the robustness of the DL because of their ability to process large data size and handle variations in biometric data such as aging or expression problem. Particularly, DL research in automatic fingerprint recognition system (AFRS) is gaining momentum starting from the last decade in the area of fingerprint pre-processing, fingerprints quality enhancement, fingerprint feature extraction, security of fingerprint and performance improvement of AFRS. However, there are limited studies that address the application of DL to model fingerprint biometric for different tasks in the fingerprint recognition process. To bridge this gap, this paper presents a systematic literature review and an insightful meta-data analysis of a decade applications of DL in AFRS. Discussion on proposed model’s tasks, state of the art study, dataset, and training architecture are presented. The Convolutional Neural Networks models were the most saturated models in developing fingerprint biometrics authentication. The study revealed different roles of the DL in training architecture of the models: feature extractor, classifier and end-to-end learning. The review highlights open research challenges and present new perspective for solving the challenges in the future. The author believed that this paper will guide researchers in propose novel fingerprint authentication scheme.

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“…Wong et. al [2,3] proposed a multi task model to enhance latent fingerprint by increasing the contrast and denoise it based CNN with very satisfied results, Imane Hachchane et. al [4] studied the face detection using fisher vector and bag of visual words with those same CNN features with satisfied results, but didn't focus on the optimizers and their effects.…”

Section: Introductionmentioning

confidence: 99%

The effect of optimizers in fingerprint classification model utilizing deep learning

Alkhalid

2020

IJEECS

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<p>Fingerprint is the most popular way to identify persons, it is assumed a unique identity, which enable us to return the record of specific person through his fingerprint, and could be useful in many applications; such as military applications, social applications, criminal applications… etc. In this paper, the study of a new model based deep learning is suggested. The focus is directed on how to enhance the training model with the increase of the testing accuracy by applying four scenarios and comparing among them. The effects of two dedicated optimizers are shown and their contrast enhancement is tested. The results prove that the testing accuracy is 85.61% for “Adadelta” optimizer, whereas for “Adam” optimizer, it is 91.73%.</p>

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Multi-task CNN for restoring corrupted fingerprint images

Cited by 51 publications

References 22 publications

Face recognition using enhancement discrete wavelet transform based on MATLAB

Face recognition using enhancement discrete wavelet transform based on MATLAB

Deep Learning Algorithms based Fingerprint Authentication: Systematic Literature Review

The effect of optimizers in fingerprint classification model utilizing deep learning

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