Design of biometrics identification system on palm vein using infrared light

2016 ◽  
Author(s):  
Muhammad Syafiq ◽  
Aulia M. T. Nasution
Keyword(s):  
Infrared Light ◽  
Identification System ◽  
Biometrics Identification ◽  
Palm Vein

scholarly journals Biometric security: Recognition according to the pattern of palm veins

2020 ◽  
Vol XXIII (1) ◽  
pp. 257-262
Author(s):  
Mitica-Valentin Manoliu
Keyword(s):  
Near Infrared ◽  
Input Image ◽  
Infrared Light ◽  
Biometric Security ◽  
Whole Process ◽  
Vein Recognition ◽  
Palm Vein

Palm vein recognition is a promising new biometric method, which has additional potential in the forensic field. This process is performed using light using NIR(Near-infrared) LEDs and the camera that captures the acquisition of veins. The obtained images have noise with variations of rotation and translation. Therefore, the input image made by the camera must be pre-processed using characteristic processes. A set of features is extracted based on images taken from infrared light cameras and processed in order to make authentication possible. This whole process can be accomplished by several methods. Thus, the application can be used to improve the security of military ships in restricted areas, but not only.

scholarly journals Infrared light sensors permit rapid recording of wingbeat frequency and bioacoustic species identification of mosquitoes

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Dongmin Kim ◽  
Terry J. DeBriere ◽  
Satish Cherukumalli ◽  
Gregory S. White ◽  
Nathan D. Burkett-Cadena
Keyword(s):  
Optical Sensors ◽  
Mosquito Species ◽  
Infrared Light ◽  
Identification System ◽  
Automated Identification ◽  
Vector Surveillance ◽  
Wingbeat Frequency ◽  
Simple Method ◽  
Species Overlap ◽  
Vector Borne

AbstractRecognition and classification of mosquitoes is a critical component of vector-borne disease management. Vector surveillance, based on wingbeat frequency and other parameters, is becoming increasingly important in the development of automated identification systems, but inconsistent data quality and results frequently emerge from different techniques and data processing methods which have not been standardized on wingbeat collection of numerous species. We developed a simple method to detect and record mosquito wingbeat by multi-dimensional optical sensors and collected 21,825 wingbeat files from 29 North American mosquito species. In pairwise comparisons, wingbeat frequency of twenty six species overlapped with at least one other species. No significant differences were observed in wingbeat frequencies between and within individuals of Culex quinquefasciatus over time. This work demonstrates the potential utility of quantifying mosquito wingbeat frequency by infrared light sensors as a component of an automated mosquito identification system. Due to species overlap, wingbeat frequency will need to integrate with other parameters to accurately delineate species in support of efficient mosquito surveillance, an important component of disease intervention.

scholarly journals Pose Invariant Palm Vein Identification System using Convolutional Neural Network

2018 ◽  
Vol 15 (4) ◽  
pp. 502-509 ◽  
Author(s):  
Baghdad Science Journal
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Data Augmentation ◽  
Region Of Interest ◽  
Input Image ◽  
Identification System ◽  
Plane Rotation ◽  
Imaging Device ◽  
Out Of Plane ◽  
Palm Vein

Palm vein recognition is a one of the most efficient biometric technologies, each individual can be identified through its veins unique characteristics, palm vein acquisition techniques is either contact based or contactless based, as the individual's hand contact or not the peg of the palm imaging device, the needs a contactless palm vein system in modern applications rise tow problems, the pose variations (rotation, scaling and translation transformations) since the imaging device cannot aligned correctly with the surface of the palm, and a delay of matching process especially for large systems, trying to solve these problems. This paper proposed a pose invariant identification system for contactless palm vein which include three main steps, at first data augmentation is done by making multiple copies of the input image then perform out-of-plane rotation on them around all the X,Y and Z axes. Then a new fast extract Region of Interest (ROI) algorithm is proposed for cropping palm region. Finally, features are extracted and classified by specific structure of Convolutional Neural Network (CNN). The system is tested on two public multispectral palm vein databases (PolyU and CASIA); furthermore, synthetic datasets are derived from these mentioned databases, to simulate the hand out-of-plane rotation in random angels within range from -20° to +20° degrees. To study several situations of pose invariant, twelve experiments are performed on all datasets, highest accuracy achieved is 99.73% ∓ 0.27 on PolyU datasets and 98 % ∓ 1 on CASIA datasets, with very fast identification process, about 0.01 second for identifying an individual, which proves system efficiency in contactless palm vein problems.

scholarly journals Design of Low-Cost Personal Identification System That Uses Combined Palm Vein and Palmprint Biometric Features

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 15922-15931 ◽  
Author(s):  
Peng Chen ◽  
Baojin Ding ◽  
Haixia Wang ◽  
Ronghua Liang ◽  
Yilong Zhang ◽  
...  
Keyword(s):  
Low Cost ◽  
Personal Identification ◽  
Identification System ◽  
Palm Vein ◽  
Biometric Features

scholarly journals Multimodal Feature-Level Fusion for Biometrics Identification System on IoMT Platform

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 21418-21426 ◽  
Author(s):  
Yang Xin ◽  
Lingshuang Kong ◽  
Zhi Liu ◽  
Chunhua Wang ◽  
Hongliang Zhu ◽  
...  
Keyword(s):  
Identification System ◽  
Feature Level Fusion ◽  
Biometrics Identification ◽  
Level Fusion

Explainable AI: A Multispectral Palm-Vein Identification System with New Augmentation Features

2021 ◽  
Vol 17 (3s) ◽  
pp. 1-21
Author(s):  
Yung-Yao Chen ◽  
Sin-Ye Jhong ◽  
Chih-Hsien Hsia ◽  
Kai-Lung Hua
Keyword(s):  
Principal Component ◽  
Image Understanding ◽  
Superior Performance ◽  
Experimental Result ◽  
Identification System ◽  
Model Parameters ◽  
Discrete Wavelet ◽  
Great Success ◽  
Explainable Ai ◽  
Palm Vein

Recently, as one of the most promising biometric traits, the vein has attracted the attention of both academia and industry because of its living body identification and the convenience of the acquisition process. State-of-the-art techniques can provide relatively good performance, yet they are limited to specific light sources. Besides, it still has poor adaptability to multispectral images. Despite the great success achieved by convolutional neural networks (CNNs) in various image understanding tasks, they often require large training samples and high computation that are infeasible for palm-vein identification. To address this limitation, this work proposes a palm-vein identification system based on lightweight CNN and adaptive multi-spectral method with explainable AI. The principal component analysis on symmetric discrete wavelet transform (SMDWT-PCA) technique for vein images augmentation method is adopted to solve the problem of insufficient data and multispectral adaptability. The depth separable convolution (DSC) has been applied to reduce the number of model parameters in this work. To ensure that the experimental result demonstrates accurately and robustly, a multispectral palm image of the public dataset (CASIA) is also used to assess the performance of the proposed method. As result, the palm-vein identification system can provide superior performance to that of the former related approaches for different spectrums.

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