Exploring Competitive Features Using Deep Convolutional Neural Network for Finger Vein Recognition

Lu, Yu; Xie, Songyun; Wu, Shiqian

doi:10.1109/access.2019.2902429

Cited by 47 publications

(20 citation statements)

References 43 publications

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“…If the big data technology is applied to the music intelligent management system, it can assist the music intelligent management system to realize the active recommendation function, which is also a relatively new way [9]. e deep convolutional neural network technology (CNN) in big data technology can better complete the data mapping relationship [10,11]. If deep CNN technology is used to map the relationship between music lovers' behavior and living environment and the music intelligent management system, it can improve the function of the music intelligent management system, which is also a better application direction.…”

Section: Introductionmentioning

confidence: 99%

Design of the Music Intelligent Management System Based on a Deep CNN

Shang

Shao

2022

Security and Communication Networks

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Music is a common art form in people’s life, and it is closely related to people’s living conditions. Since ancient times, music has been closely related to people’s lives. The music intelligent management system is convenient and user-friendly, and it can meet the demand for music. However, it has major flaws. Collaborative filtering algorithm can achieve the recommendation performance of music intelligent management system, which can recommend the same type of music to users with related preferences. Deep learning technology has developed relatively maturely, and it has been successfully applied in people’s life and production. Deep convolutional neural network (CNN) techniques can extract deeper features than simple CNN techniques. Although there is a weak nonlinear relationship between people’s behavioral characteristics and living habits and the music intelligent management system, the advantage of deep CNN technology is to deal with the nonlinear relationship between large amounts of data. This study uses deep CNN technology to extract the relationship between people’s living habits, living environment, and behavior characteristics and the music intelligent management system. The deep CNN technology helps the music intelligent management system to further realize the active recommendation function of the music intelligent management system. The research results also show that the deep CNN technology has good feasibility and high accuracy in the music intelligent management system. It can well map the relationship between people’s behavioral characteristics and living habits and the music intelligent management system. The deep CNN technology can also realize the active recommendation function of the music intelligent management system. For the prediction of the music intelligent management system, the largest prediction error is only 2.17%. This part of the error is for the prediction of song genres. The prediction errors for the other two features are both within 2%.

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Section: Introductionmentioning

confidence: 99%

Design of the Music Intelligent Management System Based on a Deep CNN

Shang

Shao

2022

Security and Communication Networks

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“…They compared the results of different configurations of the Light CNN (LCNN) and the VGG-16 models. On the other hand, Lu et al [ 121 ] presented the CNN competitive order (CNN-CO) local descriptor, which is generated by using a CNN that is pre-trained on ImageNet. After selecting the effective CNN filters from the first layer of the network, the CNN-CO computes the CNN-filtered images, builds the competitive order image, and, lastly, generates the CNN-CO pyramid histogram.…”

Section: Feature Extraction Vs Feature Learningmentioning

confidence: 99%

Feature Extraction for Finger-Vein-Based Identity Recognition

et al. 2021

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This paper aims to provide a brief review of the feature extraction methods applied for finger vein recognition. The presented study is designed in a systematic way in order to bring light to the scientific interest for biometric systems based on finger vein biometric features. The analysis spans over a period of 13 years (from 2008 to 2020). The examined feature extraction algorithms are clustered into five categories and are presented in a qualitative manner by focusing mainly on the techniques applied to represent the features of the finger veins that uniquely prove a human’s identity. In addition, the case of non-handcrafted features learned in a deep learning framework is also examined. The conducted literature analysis revealed the increased interest in finger vein biometric systems as well as the high diversity of different feature extraction methods proposed over the past several years. However, last year this interest shifted to the application of Convolutional Neural Networks following the general trend of applying deep learning models in a range of disciplines. Finally, yet importantly, this work highlights the limitations of the existing feature extraction methods and describes the research actions needed to face the identified challenges.

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“…Where ℎ , denoted as the lower half of the original input finger vein image size. Then, the preprocessing filtering mask is constructed based on using (11) and (12)…”

Section: Globalized Finger Vein Lines Extraction Based (Gmfpi)mentioning

confidence: 99%

“…Yu at el. [11] proposed a convolutional neural network (CNN) based local feature descriptor for finger vein recognition. The proposed system showed that the convolutional neural network based local feature descriptor (CNN-CO) can exploit and extract the discriminative finger fine features.…”

Section: Introductionmentioning

confidence: 99%

Double stages of feature extarction-based GFPMI for colored finger vein identification

Sulaiman

Abdulazeez

Haron

2020

IJEECS

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Today, finger vein recognition has a lot of attention as a promising approach of biometric identification framework and still does not meet the challenges of the researchers on this filed. To solve this problem, we propose s double stage of feature extraction schemes based localized finger fine image detection. We propose Globalized Features Pattern Map Indication (GFPMI) to extract the globalized finger vein line features basede on using two generated vein image datasets: original gray level color, globalized finger vein line feature, original localized gray level image, and the colored localized finger vein images. Then, two kinds of features (gray scale and texture features) are extracted, which tell the structure information of the whole finger vein pattern in the whole dataset. The recurrent based residual neural network (RNN) is used to identify the finger vein images. The experimental show that the localized colored finger vein images based globalized feature extraction has achieved the higher accuracy (93.49%) while the original image dataset achieved less accuracy by (69.86%).

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Exploring Competitive Features Using Deep Convolutional Neural Network for Finger Vein Recognition

Cited by 47 publications

References 43 publications

Design of the Music Intelligent Management System Based on a Deep CNN

Design of the Music Intelligent Management System Based on a Deep CNN

Feature Extraction for Finger-Vein-Based Identity Recognition

Double stages of feature extarction-based GFPMI for colored finger vein identification

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