scholarly journals Finger Vein Recognition Based on (2D)2PCA and Metric Learning

2012 ◽  
Vol 2012 ◽  
pp. 1-9 ◽  
Author(s):  
Gongping Yang ◽  
Xiaoming Xi ◽  
Yilong Yin
Keyword(s):  
Metric Learning ◽  
Recognition Rate ◽  
Class Imbalance ◽  
Biometric Recognition ◽  
Class Imbalance Problem ◽  
Finger Vein ◽  
Imbalance Problem ◽  
Knn Classifier ◽  
Vein Recognition ◽  
Finger Vein Recognition

Finger vein recognition is a promising biometric recognition technology, which verifies identities via the vein patterns in the fingers. In this paper, (2D)2PCA is applied to extract features of finger veins, based on which a new recognition method is proposed in conjunction with metric learning. It learns a KNN classifier for each individual, which is different from the traditional methods where a fixed threshold is employed for all individuals. Besides, the SMOTE technology is adopted to solve the class-imbalance problem. Our experiments show that the proposed method is effective by achieving a recognition rate of 99.17%.

scholarly journals Finger Vein Recognition Using Vgg-16 Cnn Algorithm

2020 ◽  
Vol 9 (8) ◽  
pp. 112-114
Keyword(s):  
Error Rate ◽  
Data Security ◽  
System Security ◽  
Biometric System ◽  
Electronic Technology ◽  
Equal Error Rate ◽  
Biometric Recognition ◽  
Finger Vein ◽  
Vein Recognition ◽  
Finger Vein Recognition

With the advancement in the electronic technology, data identification and security is to be mainly considered as a factor in the security. Biometric recognition has been taken in to consideration for security purpose. Data security has to be done to prevent the system security from transmission of data by unauthorized users. Various authentications are taken in to consideration but most commonly focuses on finger print biometric system. Biometric recognition is taken in priority which is high safe and security oriented. Preprocessing, extraction and Equal Error rate are taken in to consideration. In this we are mainly focusing in finger vein authentication domains over the system implementation.

scholarly journals Integration of Two-Dimensional Kernel Principal Component Analysis Plus Two-Dimensional Linear Discriminant Analysis with Convolutional Neural Network for Finger Vein Recognition

2021 ◽  
Vol 38 (4) ◽  
pp. 1181-1187
Author(s):  
Zhitao Gao ◽  
Jianxian Cai ◽  
Yanan Shi ◽  
Li Hong ◽  
Fenfen Yan ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Recognition Rate ◽  
Principal Component ◽  
Component Analysis ◽  
Kernel Principal Component Analysis ◽  
Two Dimensional ◽  
Linear Discriminant ◽  
Finger Vein ◽  
Vein Recognition ◽  
Finger Vein Recognition

High complexity and low recognition rate are two common problems with the current finger vein recognition methods. To solve these problems, this paper integrates two-dimensional kernel principal component analysis (K2DPCA) plus two-dimensional linear discriminant analysis (2DLDA) (K2DPCA+2DLDA) into convolutional neural network (CNN) to recognize finger veins. Considering the row and column correlations of the finger vein image matrix and the classes of finger vein images, the authors adopted K2DPCA and 2DLDA separately for dimensionality reduction and extraction of nonlinear features in row and column directions, producing a dimensionally reduced compressed image without row or column correlation. Taking the dimensionally reduced compressed image as the input, the CNN was introduced to learn higher-level features, making finger vein recognition more accurate and robust. The public dataset of Finger Vein USM (FV-USM) Database was adopted for experimental verification. The results show that the proposed approach effectively overcome the common defects of original image feature extraction: the insufficient feature description, and the redundancy of information. When the training reached 120 epochs, the model basically realized stable convergence, with the loss approaching zero and the recognition rate reaching 97.3%. Compared with two-directional two-dimensional Fisher principal component analysis ((2D)2FPCA), our strategy, which integrates K2DPCA+2DLDA with CNN, achieved a very high recognition rate of finger vein images.

A Finger Vein Recognition Method Based on PCA-RBF Neural Network

2013 ◽  
Vol 325-326 ◽  
pp. 1653-1658 ◽  
Author(s):  
Cheng Bo Yu ◽  
Jun Tan ◽  
Lei Yu ◽  
Yin Li Tian
Keyword(s):  
Neural Network ◽  
Rbf Neural Network ◽  
Recognition Rate ◽  
Back Propagation ◽  
Principal Component ◽  
Training Sample ◽  
Finger Vein ◽  
Simpler Algorithm ◽  
Vein Recognition ◽  
Finger Vein Recognition

This paper puts forward a finger vein classification algorithm which combines Principal Component Analysis (PCA) with Radial Basis Function (RBF) neural network algorithm, named the PCA-RBF algorithm. Use the training sample to reduce PCA dimensions, and abstract the main component of the image. Because of the advantages of RBF neural network classifying, put finger vein images into different classes, and then use the shortest distance to recognize. Through the experiment result comparing with Back Propagation (BP) neural network, PCA-RBF neural network is better in finger vein recognition. The result shows that PCA-RBF has faster training speed, simpler algorithm and higher recognition rate.

scholarly journals Detection of Myocardial Infarction Using ECG and Multi-Scale Feature Concatenate

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1906
Author(s):  
Jia-Zheng Jian ◽  
Tzong-Rong Ger ◽  
Han-Hua Lai ◽  
Chi-Ming Ku ◽  
Chiung-An Chen ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Network Structure ◽  
Class Imbalance ◽  
Class Imbalance Problem ◽  
Multi Scale ◽  
Imbalance Problem ◽  
Average Accuracy ◽  
Significant Difference ◽  
Electrocardiogram Ecg

Diverse computer-aided diagnosis systems based on convolutional neural networks were applied to automate the detection of myocardial infarction (MI) found in electrocardiogram (ECG) for early diagnosis and prevention. However, issues, particularly overfitting and underfitting, were not being taken into account. In other words, it is unclear whether the network structure is too simple or complex. Toward this end, the proposed models were developed by starting with the simplest structure: a multi-lead features-concatenate narrow network (N-Net) in which only two convolutional layers were included in each lead branch. Additionally, multi-scale features-concatenate networks (MSN-Net) were also implemented where larger features were being extracted through pooling the signals. The best structure was obtained via tuning both the number of filters in the convolutional layers and the number of inputting signal scales. As a result, the N-Net reached a 95.76% accuracy in the MI detection task, whereas the MSN-Net reached an accuracy of 61.82% in the MI locating task. Both networks give a higher average accuracy and a significant difference of p < 0.001 evaluated by the U test compared with the state-of-the-art. The models are also smaller in size thus are suitable to fit in wearable devices for offline monitoring. In conclusion, testing throughout the simple and complex network structure is indispensable. However, the way of dealing with the class imbalance problem and the quality of the extracted features are yet to be discussed.

scholarly journals A Novel Focal Phi Loss for Power Line Segmentation with Auxiliary Classifier U-Net

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2803
Author(s):  
Rabeea Jaffari ◽  
Manzoor Ahmed Hashmani ◽  
Constantino Carlos Reyes-Aldasoro
Keyword(s):  
Loss Function ◽  
Class Imbalance ◽  
Power Line ◽  
Aerial Images ◽  
Class Imbalance Problem ◽  
Trade Off ◽  
Urban Scenes ◽  
Imbalance Problem ◽  
A Minor ◽  
Evaluation Parameters

The segmentation of power lines (PLs) from aerial images is a crucial task for the safe navigation of unmanned aerial vehicles (UAVs) operating at low altitudes. Despite the advances in deep learning-based approaches for PL segmentation, these models are still vulnerable to the class imbalance present in the data. The PLs occupy only a minimal portion (1–5%) of the aerial images as compared to the background region (95–99%). Generally, this class imbalance problem is addressed via the use of PL-specific detectors in conjunction with the popular class balanced cross entropy (BBCE) loss function. However, these PL-specific detectors do not work outside their application areas and a BBCE loss requires hyperparameter tuning for class-wise weights, which is not trivial. Moreover, the BBCE loss results in low dice scores and precision values and thus, fails to achieve an optimal trade-off between dice scores, model accuracy, and precision–recall values. In this work, we propose a generalized focal loss function based on the Matthews correlation coefficient (MCC) or the Phi coefficient to address the class imbalance problem in PL segmentation while utilizing a generic deep segmentation architecture. We evaluate our loss function by improving the vanilla U-Net model with an additional convolutional auxiliary classifier head (ACU-Net) for better learning and faster model convergence. The evaluation of two PL datasets, namely the Mendeley Power Line Dataset and the Power Line Dataset of Urban Scenes (PLDU), where PLs occupy around 1% and 2% of the aerial images area, respectively, reveal that our proposed loss function outperforms the popular BBCE loss by 16% in PL dice scores on both the datasets, 19% in precision and false detection rate (FDR) values for the Mendeley PL dataset and 15% in precision and FDR values for the PLDU with a minor degradation in the accuracy and recall values. Moreover, our proposed ACU-Net outperforms the baseline vanilla U-Net for the characteristic evaluation parameters in the range of 1–10% for both the PL datasets. Thus, our proposed loss function with ACU-Net achieves an optimal trade-off for the characteristic evaluation parameters without any bells and whistles. Our code is available at Github.

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