scholarly journals HIGH ACCURACY RECOGNITION BIO METRICS BASED ON FINGER VEIN SCREENING SENSOR

2020 ◽  
Vol 3 (2) ◽  
pp. 35-46
Author(s):  
Shereen S. Jumaa ◽  
Khamis A. Zidan
Keyword(s):  
Nearest Neighbor ◽  
Low Cost ◽  
Computation Time ◽  
Recognition System ◽  
K Nearest Neighbor ◽  
Finger Print ◽  
Finger Vein ◽  
Vein Recognition ◽  
Histogram Of Gradients ◽  
Micro Lens

One of the safest biometrics of today is finger vein- but this technic  arises with some specific challenges, the most common  one being that the vein pattern is hard to extract because finger vein images are always low in quality, significantly  hampered the feature extraction and classification stages. Professional  algorithms want to be considered with the conventional hardware for capturing finger-vein images is  by using red Surface Mounted Diode (SMD) leds for this aim. For capturing images, Canon 750D camera with micro lens is used. For high quality images the integrated micro lens  is used, and with some adjustments it can also obtain finger print. Features extraction was used by a combination of Hierarchical Centroid and Histogram of Gradients. Results were evaluated with K Nearest Neighbor and Deep Neural Networks using 6 fold stratified cross validation. Results displayed improvement as compared to three latest benchmarks in this field that used 6-fold validation and SDUMLA-HMT. The work novelty is owing to the hardware design of the sensor within the finger-vein recognition system to obtain, simultaneously, highly secured recognition with low computation time ,finger vein and finger print at low cost, unlimited users for one device and open source.

Near Infrared Finger Vein Recognition Method Based on Subspace Projection Technology

2014 ◽  
Vol 1030-1032 ◽  
pp. 2382-2385 ◽  
Author(s):  
Lin Lin Fan ◽  
Hui Ma ◽  
Ke Jun Wang ◽  
Yong Liang Shen ◽  
Ying Shi ◽  
...  
Keyword(s):  
Near Infrared ◽  
Nearest Neighbor ◽  
Recognition Performance ◽  
Recognition System ◽  
Critical Issue ◽  
Subspace Projection ◽  
Threshold Method ◽  
Finger Vein ◽  
Vein Recognition ◽  
Finger Vein Recognition

Finger vein recognition refers to a recent biometric technique which exploits the vein patterns in the human finger to identify individuals. Finger vein recognition faces a number of challenges. One critical issue is the performance of finger vein recognition system. To overcome this problem, a finger vein recognition algorithm based on one kind of subspace projection technology is presented. Firstly, we use Kapur entropy threshold method to achieve the purpose of intercepting region of finger under contactless mode. Then the finger vein features were extracted by 2DPCA method. Finally, we used of nearest neighbor distance classifier for matching. The results indicate that the algorithm has good recognition performance.

Finger Vein Recognition Using Local Mean Based K-Nearest Centroid Neighbor Classifier

2012 ◽  
Vol 628 ◽  
pp. 427-432 ◽  
Author(s):  
Ali Khalili Mobarakeh ◽  
Sayedmehran Mirsafaie Rizi ◽  
Saba Nazari ◽  
Jiang Ping Gou ◽  
Bakhtiar Affendi Rosdi
Keyword(s):  
Nearest Neighbor ◽  
Identification System ◽  
K Nearest Neighbor ◽  
Recognition Method ◽  
Finger Vein ◽  
New Type ◽  
Local Mean ◽  
Vein Recognition ◽  
Finger Vein Recognition ◽  
Neighbor Classifier

One of the newest methods of identification system is finger vein recognition which is a unique and successful way to identify human based on the physical characteristics of finger vein patterns. In this paper, a new type of classifier called Local Mean based K-nearest centroid neighbor (LMKNCN) is applied to classify finger vein patterns. Finally, the significance of the proposed method is proven by comparing the results of LMKNCN classifier with traditionally used K nearest neighbor classifier (KNN). The experimental results indicate that the proposed method in this research confidently merits the performance of the finger vein recognition method, as the gained accuracy using the proposed method is higher than that of the traditionally used method KNN. The maximum obtained accuracy of LMKNCN test with 2040 number of finger vein images is 100% while for KNN is 98.53%.

IRIS AND FINGER VEIN MULTI MODEL RECOGNITION SYSTEM BASED ON SIFT FEATURES

2018 ◽  
Vol 1 (2) ◽  
pp. 34-44
Author(s):  
Faris E Mohammed ◽  
Dr. Eman M ALdaidamony ◽  
Prof. A. M Raid
Keyword(s):  
Iris Recognition ◽  
Recognition Performance ◽  
Recognition System ◽  
Individual Identification ◽  
Work Place ◽  
Identification Process ◽  
Finger Vein ◽  
Noise Point ◽  
Vein Recognition ◽  
A New Technique

Individual identification process is a very significant process that resides a large portion of day by day usages. Identification process is appropriate in work place, private zones, banks …etc. Individuals are rich subject having many characteristics that can be used for recognition purpose such as finger vein, iris, face …etc. Finger vein and iris key-points are considered as one of the most talented biometric authentication techniques for its security and convenience. SIFT is new and talented technique for pattern recognition. However, some shortages exist in many related techniques, such as difficulty of feature loss, feature key extraction, and noise point introduction. In this manuscript a new technique named SIFT-based iris and SIFT-based finger vein identification with normalization and enhancement is proposed for achieving better performance. In evaluation with other SIFT-based iris or SIFT-based finger vein recognition algorithms, the suggested technique can overcome the difficulties of tremendous key-point extraction and exclude the noise points without feature loss. Experimental results demonstrate that the normalization and improvement steps are critical for SIFT-based recognition for iris and finger vein , and the proposed technique can accomplish satisfactory recognition performance. Keywords: SIFT, Iris Recognition, Finger Vein identification and Biometric Systems.   © 2018 JASET, International Scholars and Researchers Association    

Handwritten Balinesse Character Recognition using K-Nearest Neighbor

2018 ◽  
Author(s):  
I Wayan Agus Surya Darma
Keyword(s):  
Feature Extraction ◽  
Success Rate ◽  
Character Recognition ◽  
Nearest Neighbor ◽  
Recognition System ◽  
Extraction Process ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Algorithm ◽  
K Nearest Neighbor Algorithm ◽  
Character Feature

Balinese character recognition is a technique to recognize feature or pattern of Balinese character. Feature of Balinese character is generated through feature extraction process. This research using handwritten Balinese character. Feature extraction is a process to obtain the feature of character. In this research, feature extraction process generated semantic and direction feature of handwritten Balinese character. Recognition is using K-Nearest Neighbor algorithm to recognize 81 handwritten Balinese character. The feature of Balinese character images tester are compared with reference features. Result of the recognition system with K=3 and reference=10 is achieved a success rate of 97,53%.

scholarly journals COVIMOS: A Coastal Video Monitoring System

2017 ◽  
Vol 1 (1) ◽  
pp. 1
Author(s):  
I Made Oka Widyantara ◽  
I Made Dwi Asana Putra ◽  
Ida Bagus Putu Adnyana
Keyword(s):  
Monitoring System ◽  
Nearest Neighbor ◽  
Low Cost ◽  
Video Image ◽  
Video Monitoring ◽  
Reference Object ◽  
Self Organizing Map ◽  
K Nearest Neighbor ◽  
Ip Camera ◽  
Class Labels

This paper intends to explain the development of Coastal Video Monitoring System (CoViMoS) with the main characteristics including low-cost and easy implementation. CoViMoS characteristics have been realized using the device IP camera for video image acquisition, and development of software applications with the main features including detection of shoreline and it changes are automatically. This capability was based on segmentation and classification techniques based on data mining. Detection of shoreline is done by segmenting a video image of the beach, to get a cluster of objects, namely land, sea and sky, using Self Organizing Map (SOM) algorithms. The mechanism of classification is done using K-Nearest Neighbor (K-NN) algorithms to provide the class labels to objects that have been generated on the segmentation process. Furthermore, the classification of land used as a reference object in the detection of costline. Implementation CoViMoS system for monitoring systems in Cucukan Beach, Gianyar regency, have shown that the developed system is able to detect the shoreline and its changes automatically.

scholarly journals Feasibility of Using Floor Vibration to Detect Human Falls

2020 ◽  
Vol 18 (1) ◽  
pp. 200
Author(s):  
Yu Shao ◽  
Xinyue Wang ◽  
Wenjie Song ◽  
Sobia Ilyas ◽  
Haibo Guo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Human Body ◽  
Nearest Neighbor ◽  
Body Movement ◽  
Modern Society ◽  
Recognition System ◽  
Machine Learning Algorithms ◽  
K Nearest Neighbor ◽  
Study Results ◽  
Floor Vibration

With the increasing aging population in modern society, falls as well as fall-induced injuries in elderly people become one of the major public health problems. This study proposes a classification framework that uses floor vibrations to detect fall events as well as distinguish different fall postures. A scaled 3D-printed model with twelve fully adjustable joints that can simulate human body movement was built to generate human fall data. The mass proportion of a human body takes was carefully studied and was reflected in the model. Object drops, human falling tests were carried out and the vibration signature generated in the floor was recorded for analyses. Machine learning algorithms including K-means algorithm and K nearest neighbor algorithm were introduced in the classification process. Three classifiers (human walking versus human fall, human fall versus object drop, human falls from different postures) were developed in this study. Results showed that the three proposed classifiers can achieve the accuracy of 100, 85, and 91%. This paper developed a framework of using floor vibration to build the pattern recognition system in detecting human falls based on a machine learning approach.

scholarly journals Finger-Vein Recognition Using Heterogeneous Databases by Domain Adaption Based on a Cycle-Consistent Adversarial Network

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 524
Author(s):  
Kyoung Jun Noh ◽  
Jiho Choi ◽  
Jin Seong Hong ◽  
Kang Ryoung Park
Keyword(s):  
Domain Adaptation ◽  
Recognition Accuracy ◽  
Recognition System ◽  
Heterogeneous Databases ◽  
Second Best ◽  
Finger Vein ◽  
Adversarial Network ◽  
Image Characteristics ◽  
Vein Recognition ◽  
Finger Vein Recognition

The conventional finger-vein recognition system is trained using one type of database and entails the serious problem of performance degradation when tested with different types of databases. This degradation is caused by changes in image characteristics due to variable factors such as position of camera, finger, and lighting. Therefore, each database has varying characteristics despite the same finger-vein modality. However, previous researches on improving the recognition accuracy of unobserved or heterogeneous databases is lacking. To overcome this problem, we propose a method to improve the finger-vein recognition accuracy using domain adaptation between heterogeneous databases using cycle-consistent adversarial networks (CycleGAN), which enhances the recognition accuracy of unobserved data. The experiments were performed with two open databases—Shandong University homologous multi-modal traits finger-vein database (SDUMLA-HMT-DB) and Hong Kong Polytech University finger-image database (HKPolyU-DB). They showed that the equal error rate (EER) of finger-vein recognition was 0.85% in case of training with SDUMLA-HMT-DB and testing with HKPolyU-DB, which had an improvement of 33.1% compared to the second best method. The EER was 3.4% in case of training with HKPolyU-DB and testing with SDUMLA-HMT-DB, which also had an improvement of 4.8% compared to the second best method.

An Arcloss-Based and Openset-Test-Oriented Finger Vein Recognition System

2021 ◽  
pp. 287-294
Author(s):  
Zhenxiang Chen ◽  
Wangwang Yu ◽  
Haohan Bai ◽  
Yongjie Li
Keyword(s):  
Recognition System ◽  
Finger Vein ◽  
Vein Recognition ◽  
Finger Vein Recognition

Improving the Performance of kNN in the MapReduce Framework Using Locality Sensitive Hashing

2019 ◽  
Vol 10 (4) ◽  
pp. 1-16
Author(s):  
Sikha Bagui ◽  
Arup Kumar Mondal ◽  
Subhash Bagui
Keyword(s):  
Nearest Neighbor ◽  
Parallel Implementation ◽  
Block Size ◽  
Computation Time ◽  
Locality Sensitive Hashing ◽  
K Nearest Neighbor ◽  
Mapreduce Framework ◽  
Data Set ◽  
Data Object ◽  
Very Large Datasets

In this work the authors present a parallel k nearest neighbor (kNN) algorithm using locality sensitive hashing to preprocess the data before it is classified using kNN in Hadoop's MapReduce framework. This is compared with the sequential (conventional) implementation. Using locality sensitive hashing's similarity measure with kNN, the iterative procedure to classify a data object is performed within a hash bucket rather than the whole data set, greatly reducing the computation time needed for classification. Several experiments were run that showed that the parallel implementation performed better than the sequential implementation on very large datasets. The study also experimented with a few map and reduce side optimization features for the parallel implementation and presented some optimum map and reduce side parameters. Among the map side parameters, the block size and input split size were varied, and among the reduce side parameters, the number of planes were varied, and their effects were studied.

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