Finger vein recognition based on deep learning

Video: Finger Vein Recognition Using Deep Learning SpringerLin

In this paper, we have investigated the finger vein recognition problem. We have used deep convolutional neural network models for feature extraction purposes on two commonly used publicly available finger vein datasets. To improve the performance further on unseen data for verification purposes, we have employed one-shot learning model namely. Using Deep Learning for Finger-vein Recognition From the first famous neural networks LeNet to identify images of 10 handwritten digits, to much more complex neural networks to classify 1000 classes of images in ImageNet, deep neural networks (DNNs), especially convolutional neural networks (CNNs) are well known for their power in computer vision Abstract: Deep learning has recently achieved impressive performance in the area of biometric recognition. The technology of finger vein recognition possesses better anti forgery performance and identification stability in collecting and certificating information of human bodies Finger Vein Recognition Based on Multi-Task Learning. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770--778). Google Scholar Cross Ref; Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster r-cnn: Towards real-time object detection with region proposal.

Using Deep Learning for finger-vein based biometric

  1. Securing Deep Learning Based Edge Finger Vein Biometrics With Binary Decision Diagram Abstract: With built-in artificial intelligence (AI), edge devices, e.g., smart cameras, can perform tasks like detecting and tracking individuals, which is referred to as edge biometrics. As a driving force for AI, machine/deep learning plays a critical role.
  2. Finger-vein recognition is another biometric authentication method. Other biometric authentication systems based on fingerprints, iris, voice, and facial image are possible to be fooled with fake.
  3. Robust Finger Vein Recognition Based on Deep CNN with Spatial Attention and Bias Field Correction. Zhe Huang we propose a novel CNN-based finger vein recognition approach with bias field correction, spatial attention mechanism and a multistage transfer learning strategy to cope with the difficulties mentioned above. and the multistage.
  4. deep learning techniques for biometric recognition purposes [60], due to the good performance they achieve. In this section, we present an overview of the most relevant papers using deep learning methods in the field of vein-based biometrics. The related details are summarized in Table II. A deep learning approach applied to a finger-vein-based
  5. proposed an adaptive Gabor convolutional neural networks for finger vein recognition. Results show that this method will reduce the number of parameters and the speed of processing row data. There are some advantages and disadvantages in finger vein recognition with deep learning
  6. However, the biggest challenge is how to protect biometric data while keeping the practical performance of identity verification systems. For the sake of tackling this problem, this paper presents a novel finger vein recognition algorithm by using secure biometric template scheme based on deep learning and random projections, named FVR-DLRP
  7. Finger vein identification is a recently developed biometric technology and has become an essential field in biometrics, garnering increasing attention in recent years. As a biometric trait, using vein patterns allows for personal recognition with high security. In this paper, we have employed an improved deep network, named Merge Convolutional.

Although the dataset for finger vein recognition is small, the performance of deep learning finger vein recognition is remarkable. Deep learning finger vein identification method performance can be enhanced by employing large datasets, so there is a need for a large finger vein image dataset Hu et al. proposed FV-Net based on deep learning, which obtained the recognition result by matching the finger-vein feature subregion extracted by CNN. Since then, more and more scholars have applied CNN to extract in-depth finger veins and built models with strong robustness and high classification accuracy [ 19 ] Recently, the deep learning framework has been successfully applied in computer vision and delivered superior results compared to traditional handcrafted methods on various computer vision applications such as image-based face recognition, gender recognition and image classification. (NIR) camera-based finger-vein recognition system using.

biometrics recognition system based on the deep learning method that uses convolutional neural networks (CNNs). The authors propose two multimodal architectures using the finger knuckle print (FKP) and the finger vein (FV) biometrics with different levels of fusion: the features level fusion and scores level fusion segmentation-based methods, as well as for techniques based on statistics, finger rotation and translation have a negati ve impact on recognition performance. In order to overcome such limitations, in this paper we pro-pose to perform finger-vein-based identification by exploiting deep-learning techniques. Deep learning is mainly inspired b Multimodal biometric recognition systems using deep learning based on the finger vein and finger knuckle print fusion. Sara Daas, Corresponding Author. Fig. 4 represents the process of finger recognition based on score level fusion recognised also as fusion after matching. The fusion is achieved by the combination of scores from different.

Article: A finger vein recognition algorithm based on deep

(PDF) Deep learning-based Region of Interest Extraction

The modern-day finger vein based human recognition techniques provide good performance, yet they are highly finger vein image quality dependent. To address this problem, a novel deep learning-based approach using convolution-neural-network (CNN) for finger vein identification has been introduced here DOI: 10.1007/s00500-017-2487-9 Corpus ID: 3885447. Finger vein secure biometric template generation based on deep learning @article{Liu2018FingerVS, title={Finger vein secure biometric template generation based on deep learning}, author={Y. Liu and Jie Ling and Zhusong Liu and Jian Shen and Chongzhi Gao}, journal={Soft Computing}, year={2018}, volume={22}, pages={2257-2265} In this regard, this paper introduces a scheme for multimodal biometric recognition system based on the fusion of finger-vein and face images using Convolutional Neural Network (CNN) and different classifiers. The pre-processed finger-vein image using Adaptive Histogram Equalization (AHE) is input into a CNN model

Biometric technology has played an important role in our lives. Finger vein recognition has many advantages as a new biometric technology. Different from iris, fingerprint or face, finger vein recognition is cheap, Non-contact, living. However, finger vein recognition is susceptible to many factors such as light environment, finger moving, etc. In order to solve these problems, in this paper. We have proposed, recently, the first deep neural network (DNN) framework for assessing finger-vein quality, that does not require manual labeling of high and low quality images, as is the case for state of the art methods, but infers such annotations automatically based on an objective indicator, the biometric verification decision 1. Introduction. Vein recognition [1-9] is an emerging biometric technology that uses the veins in hands for authentication. Compared with traditional biometrics such as fin-gerprint recognition [10], face recognition [11,12] and iris recognition [13], vein recognition has the advantages of non-contact, living body recognition, and high security Research. My research area is the application of Pattern recognition, Machine Learning and Deep Learning in Biometrics and Machine Vision applications. Currently, I have two main projects, which are : Finger based biometric system and machine vision for defect detection

Deep learning has recently achieved impressive performance in the area of biometric recognition. The technology of finger vein recognition possesses better anti forgery performance and identification stability in collecting and certificating information of human bodies In recent years, deep learning has received an excellent performance in the tasks of image feature extraction and image classification. Besides, the coding-based methods have been widely focused on because of their outstanding local description. In this paper, we propose a novel method for finger-vein recognition, which combines local coding and convolution neural network (LC-CNN) In addition, deep learning-based image recognition methods, which show high recognition performance through big data learning, are being applied to various fields, and as a part of this effort, finger-vein recognition using CNN has been researched. Radzi et al. proposed a metho

Finger Vein Recognition Based on Multi-Task Learning

Securing Deep Learning Based Edge Finger Vein Biometrics

CONFERENCE PROCEEDINGS Papers Presentations Journals. Advanced Photonics Journal of Applied Remote Sensin In order to further improve the recognition accuracy, in this paper we propose an end-to-end method for recognizing Hand dorsal vein Based on Deep hash network (DHN), called HBD. The hand dorsal vein image is input into the simplified Convolutional Neural Networks-Fast (SCNN-F) to obtain convolution features Deep Belief Network (DBN) for vein image recognition. Experimental results show that our scheme is effective and better than existing schemes. Keywords: Finger vein recognition, Deep learning, Deep belief network, Uniform local binary pattern, Curvature gray image 1. Introduction. Driven by high-tech such as big data and cloud computing, huma

Feature component-based extreme learning machines for finger vein recognition. Cognitive Computation, 6(3):446-461, 2014. [3] R Prem Kumar, Rachit Agrawal, Surbhi Sharma, Malay Kishore Dutta, Carlos M Travieso, Jesus B Alonso-Hern´ andez, et al.´Finger vein recognition using integrated responses of texture features. In 2015 4th. Keywords CNN, Multimodal biometrics, Fingerprint recognition, Finger-vein recognition, Face recognition, Fusion, Random forest The CNN is a convolutional neural network based on deep supervised learning model. In this regard, CNN can be viewed an automatic feature extractor and a trainabl

The Schematic diagram of training CNN with the progressive

deep-learning dataset gender-recognition datasets gender-classification biometric-identification deep-neural-network biometric-authentication Authenticating users based on style of how they type. python3 keyboard-events biometric-identification Bio-metric Identification of a person using Finger Vein Data Finger vein recognition/match using python & opencv (2019.12) My personal project for the final assignment of Class Machine Vision Application in SCUT. Using traditional ways to recognition/match images of finger vein. An Intelligent Classroom Attendance System Based on Dynamic Face Recognition (2018.05 - 2019.05 23.33% by Flann based matching. The two Finger veins do not match. 6. CONCLUSION Finger vein recognition systems in biometric security have been proved robust for safety in digital access. The system can easily detect differences between stored finger vein images of the person and the person may try to access the account in a secure manner In this research work, a new approach based on actors for parallel implementation of OTSU's binarization for 20 images of hand vein, based on communicatio

Scores for the CNN-based method

The fingervein recognition is based on human veins characteristic for identification or verification of the individual multimodal biometric system based on Iris, finger vein and fingerprint was investigated The CNN is a convolutional neural network based on deep supervised learning model Chapter 1 Deep Learning-Based Hyperspectral Multimodal Biometric. Authentication System Using Palmprint and Dorsal Hand Vein. Shuping Zhao, Wei Nie, and Bob Zhang. Chapter 2 Cancelable Biometrics for Template Protection: Future. Directives with Deep Learning. Avantika Singh, Gaurav Jaswal, and Aditya Niga

Average classification rates and classification times (inInam ULLAH | PhD Student | Shandong University, Jinan

From idea to AI deployment: using deep learning for finger

Integrated Automated Fingerprint Identification System (IAFIS). The brief description of the three systems helps us to classify the fingerprints for a Deep Learning project. A. Henry Fingerprint Classification Systems (HFCS) Henry classification system gives a number to each finger based on finger pattern type Biometric recognition based on finger-vein patterns is gaining more and more attention, as several approaches have been recently proposed to extract discriminative features from vascular structures. In this paper we investigate the similarity between vein patterns of symmetric fingers of the left and right hand of a subject. More in detail, we analyze the performance achievable when using. Palm Vein Recognition. RFID Technology. GPS Location or Wi-Fi. QR Code-based Technology. Bluetooth-based Technology. Fingerprint Technology. PIN. Blog Reading Time: 6 minutes. There is a growing demand for different types of user authentication technologies for physical assets, human assets, and intellectual properties Finger vein recognition based on deep learning W Liu, W Li, L Sun, L Zhang, P Chen 2017 12th IEEE conference on industrial electronics and applications (ICIEA , 201 Feature-Level Fusion of Finger Vein and Fingerprint Based on a Single Finger Image: The Use of an Incompletely Closed Near-Infrared Equipment. Symmetry (5 ed., vol. 12, pp. 709). Multidisciplinary Digital Publishing Institute

Robust Finger Vein Recognition Based on Deep CNN with

29 FEBA -An Anatomy Based Finger Vein Classification 31 Face Quality Estimation and Its Correlation to Demographic and Non-Demographic Bias in Face Recognition 41 Cross-Spectral Periocular Recognition with Conditional Adversarial Networks 42 Learning to Learn Face-PAD: a lifelong learning approac Unsupervised Learning can also be used for full automatic finger vein pattern extraction. Unsupervised Learning in Fingerprint recognition-The fingerprint recognition was improved with an iterative Expectation-Maximization algorithm for collusion strategy adaptation Current research topics are for example face recognition at a distance, intelligent video surveillance, biometrics for border control, finger vein recognition, 3D face modelling and recognition, deep learning, forensic biometrics and detection of manipulation of photographs or videos Topics and features: addresses the application of deep learning to enhance the performance of biometrics identification across a wide range of different biometrics modalities; revisits deep learning for face biometrics, offering insights from neuroimaging, and provides comparison with popular CNN-based architectures for face recognition. Downloadable! Face recognition is increasingly being used for solving various social-problems such as personal protection and authentication. As with other widely used biometric applications, facial recognition is a biometric instrument such as iris recognition, vein pattern recognition, and fingerprint recognition. Facial recognition identifies a person based on certain aspects of his physiology

Fusion loss and inter-class data augmentation for deep finger vein feature learning Research output : Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-revie Rice is the most important grain in Thailand for both consuming and exporting. One of the critical problems in rice cultivation is rice diseases, which affects directly to the yield. Early disease recognition is handled by a human, which is difficult to achieve high accuracy and the performance depends on the farmer's experience. To overcome this problem, we did three folds of contributions The invention discloses a gait recognition method based on deep learning. The gait recognition method based on deep learning is characterized that identity of a person in a video is recognized according to gaits of the person through weight-shared two-channel convolutional neural networks by utilizing strong learning ability of the convolutional neural networks in a deep learning mode Evaluation of wavelet transform preprocessing with deep learning aimed at palm vein recognition application AIP Conference Proceedings Palm vein recognition based on 2D-discrete wavelet transform and linear discrimination Meng, P. Fang, and B. Zhang, Finger vein recognition based on convolutional neural network, in.

Sensors | Special Issue : Visual Sensors

Finger vein recognition based on lightweight CNN combining

Instead, inspired by the recent success of Deep Learning in several vision tasks, and by the ability of the technique to leverage data, we focus on two general-purpose approaches to build image-based anti-spoofing systems with convolutional networks for several attack types in three biometric modalities, namely iris, face, and fingerprint 146: Iris Presentation Attack Detection by Attention-based and Deep Pixel-wise Binary Supervision Network; 151: MixFaceNets: Extremely Efficient Face Recognition Networks; 152: Vulnerability Assessment and Presentation Attack Detection Using a Set of Distinct Finger Vein Recognition Algorithm Revisits deep learning for face biometrics, offering insights from neuroimaging, and provides comparison with popular CNN-based architectures for face recognition Examines deep learning for state-of-the-art latent fingerprint and finger-vein recognition, as well as iris recognition

Finger vein secure biometric template generation based on

Human identification plays a vital role in daily lives. A majority of biometric technologies require the active cooperation of humans, while gait recognition does not. Compared with other identification technologies, radar-based technology can monitor the human body around the clock without being affected by light/weather, and is not easy to be forged while protecting privacy Contactless Time Attendance Biometric Products . Contactless like Face & Iris Recognition which provides great and effortless user experience. Our Face & Iris Recognition is based on innovative, deep learning technology, designed to meet distinctive user identification needs of the organizations This book constitutes the refereed proceedings of the 11th Chinese Conference on Biometric Recognition, CCBR 2016, held in Chengdu, China, in October 2016. The 84 revised full papers presented in this book were carefully reviewed and selected from 138 submissions. The papers focus on Face Recognition and Analysis; Fingerprint, Palm-print and Vascular Biometrics; Iris and Ocular Biometrics.

Finger vein identification using deeply-fused

Deep Learning approach for Multimodal Bio-metric. recognition System with Fusion based on Face , Iris and Finger Vein Pattern (batch id : 2021/A/02) PRESENT BY: CH.LAKSHMI PRASANNA-178X1A0518 G.V.J.N.S.LAKSHMI KUMARI-178X1A0529 KANAPARTHI.ANUSHA-178X1A0539 KUNCHALA.PADMAJA-178X1A0556 GUIDE NAME: Mr.S.SURESH BABU ASSOCIATE PROFESSOR • Objective • Abstract • Existing system Contents. Ghazi MM, Ekenel HK, A comprehensive analysis of deep learning based representation for face recognition. In: 2016 IEEE conference on computer vision and pattern recognition workshops (CVPRW). Las Vegas, NV: IEEE; 2016. pp. 102-109. 25. Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, et al. ImageNet large scale visual recognition. Online Library Speech Recognition Using Deep Learning Algorithms Modern Multidimensional Scaling This book provides a comprehensive overview of the recent advancement in the field of automatic speech recognition with a focus on deep learning models including deep neural networks and many of their variants From 2015 to the 2019 edition, deep-learning-based algorithms outnumbered the ones based on handcraft features. In this webinar, we firstly review the main techniques for fingerprint presentation attack detection. We also summarize the LivDet experience. Finally, we provide some anticipations of what we expect by the incoming Livdet 2021 Biometric deals with the verification and identification of a person based on behavioural and physiological traits. This article presents recent advances in physiological-based biometric multimodalities, where we focused on finger vein, palm vein, fingerprint, face, lips, iris, and retina-based processing methods

A Systematic Review of Finger Vein Recognition Technique

The last research session of the first day focused on fingerprint recognition. On the one hand, a new deep-learning-based approach for fingerprint pre-alignment using Siamese networks was presented. On the other hand a novel 2400 dpi finger acquisition device was introduced which can be used to capture neonate fingerprints identifying humans based on a finger vein pattern. The system uses a database of human fingerprint images obtained at infrared range. The current proposal used a Sobel detector, an upgrade filter and a capture process to obtain a vein pattern. The proposed system is implemented using the novel fingerprint recognition algorithm Finger Vein Recognition Product; Solutions. Face Recognition Face recognition technology is based on human facial features, it is the input face image or Video streaming. First to judge whether there is a face, if there is a face, it will show the location and size of each face and the location information for main facial organs. Trampoline Motion Decomposition Method Based on Deep Learning Image Recognition. Yushan Liu,1 Huijuan Dong,2 and Liang Wang 3. 1Institute of Physical Education., North Minzu University, Yinchuan, Ningxia 750021, China. 2Hebei Sport University, Shijiazhuang, Hebei 050021, China

With built-in artificial intelligence (AI), edge devices, e.g., smart cameras, can perform tasks like detecting and tracking individuals, which is referred to as edge biometrics. As a driving force for AI, machine/deep learning plays a critical role in edge biometrics. However, research shows that artificial neural networks, e.g., convolutional neural networks, are invertible such that. Previous studies were mostly based on finger-print, palm vein etc. however, due to being more secure than fingerprint system and due to the fact that each person's finger vein is different from others finger vein are impossible to use to do forgery as veins reside under the skin FINGER VEIN RECOGNITION Dr.S.Brindha1 1HoD, Department of Computer Networking, PSG Polytechnic College, Coimbatore, Tamilnadu, India -----***----- Abstract - Vein based biometrics are gaining importance due to their greater accuracy and security. Finger vein based biometric systems elegantly address problems present in fingerprint systems