A Palm Vein Recognition System based on a Support Vector Machine

2019 ◽  
Vol 8 (1) ◽  
pp. 1-7
Author(s):  
Vijayakumar Ponnusamy ◽  
Abhijit Sridhar ◽  
Arun Baalaaji ◽  
M. Sangeetha
Keyword(s):  
Support Vector Machine ◽  
Recognition System ◽  
Support Vector ◽  
Vein Recognition ◽  
Palm Vein

scholarly journals An Open Source Contact-Free Palm Vein Recognition System

2017 ◽  
Vol 6 (4) ◽  
pp. 319
Author(s):  
Ranjith Kumar M ◽  
Deepika G G ◽  
Meenakshi Krishnan ◽  
Karthikeyan B
Keyword(s):  
Open Source ◽  
Region Of Interest ◽  
Recognition System ◽  
Computational Time ◽  
Raspberry Pi ◽  
Support Vector ◽  
Vein Recognition ◽  
Palm Vein ◽  
Open Source Hardware ◽  
Time Systems

In this document, we propose a novel palm vein<em> </em>recognition system using open source hardware and software. We have developed an alternative preprocessing and feature extraction technique. The proposed system is built on Raspberry Pi using OpenCV 2.4.12. The palm vein image is cropped to Region of Interest(ROI) to reduce the computational time in real time systems and then preprocessed to enhance the vein pattern visibility and to extract more number of key points using SIFT algorithm. Then the descriptors are stored in a dictionary like codebook file during training. Later the descriptors are tested with unknown patterns. The clustering is based on K-means algorithm and classification is done using Support Vector Machines (SVM).

OFFLINE YORÙBÁ HANDWRITTEN WORD RECOGNITION USING GEOMETRIC FEATURE EXTRACTION AND SUPPORT VECTOR MACHINE CLASSIFIER

2020 ◽  
Vol 5 (2) ◽  
pp. 504
Author(s):  
Matthias Omotayo Oladele ◽  
Temilola Morufat Adepoju ◽  
Olaide ` Abiodun Olatoke ◽  
Oluwaseun Adewale Ojo
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Word Recognition ◽  
Support Vector Machine Classifier ◽  
Recognition Accuracy ◽  
Recognition System ◽  
Support Vector ◽  
Geometric Features ◽  
Total Length ◽  
Yoruba Language

Yorùbá language is one of the three main languages that is been spoken in Nigeria. It is a tonal language that carries an accent on the vowel alphabets. There are twenty-five (25) alphabets in Yorùbá language with one of the alphabets a digraph (GB). Due to the difficulty in typing handwritten Yorùbá documents, there is a need to develop a handwritten recognition system that can convert the handwritten texts to digital format. This study discusses the offline Yorùbá handwritten word recognition system (OYHWR) that recognizes Yorùbá uppercase alphabets. Handwritten characters and words were obtained from different writers using the paint application and M708 graphics tablets. The characters were used for training and the words were used for testing. Pre-processing was done on the images and the geometric features of the images were extracted using zoning and gradient-based feature extraction. Geometric features are the different line types that form a particular character such as the vertical, horizontal, and diagonal lines. The geometric features used are the number of horizontal lines, number of vertical lines, number of right diagonal lines, number of left diagonal lines, total length of all horizontal lines, total length of all vertical lines, total length of all right slanting lines, total length of all left-slanting lines and the area of the skeleton. The characters are divided into 9 zones and gradient feature extraction was used to extract the horizontal and vertical components and geometric features in each zone. The words were fed into the support vector machine classifier and the performance was evaluated based on recognition accuracy. Support vector machine is a two-class classifier, hence a multiclass SVM classifier least square support vector machine (LSSVM) was used for word recognition. The one vs one strategy and RBF kernel were used and the recognition accuracy obtained from the tested words ranges between 66.7%, 83.3%, 85.7%, 87.5%, and 100%. The low recognition rate for some of the words could be as a result of the similarity in the extracted features.

GESTURE RECOGNITION SYSTEM FOR NIGERIAN TRIBAL GREETING POSTURES USING SUPPORT VECTOR MACHINE

2020 ◽  
Vol 5 (2) ◽  
pp. 609
Author(s):  
Segun Aina ◽  
Kofoworola V. Sholesi ◽  
Aderonke R. Lawal ◽  
Samuel D. Okegbile ◽  
Adeniran I. Oluwaranti
Keyword(s):  
Support Vector Machine ◽  
Gesture Recognition ◽  
Recognition Rate ◽  
Recognition Task ◽  
Recognition System ◽  
Human Interaction ◽  
Support Vector ◽  
System A ◽  
Extraction Algorithm ◽  
Gaussian Blur

This paper presents the application of Gaussian blur filters and Support Vector Machine (SVM) techniques for greeting recognition among the Yoruba tribe of Nigeria. Existing efforts have considered different recognition gestures. However, tribal greeting postures or gestures recognition for the Nigerian geographical space has not been studied before. Some cultural gestures are not correctly identified by people of the same tribe, not to mention other people from different tribes, thereby posing a challenge of misinterpretation of meaning. Also, some cultural gestures are unknown to most people outside a tribe, which could also hinder human interaction; hence there is a need to automate the recognition of Nigerian tribal greeting gestures. This work hence develops a Gaussian Blur – SVM based system capable of recognizing the Yoruba tribe greeting postures for men and women. Videos of individuals performing various greeting gestures were collected and processed into image frames. The images were resized and a Gaussian blur filter was used to remove noise from them. This research used a moment-based feature extraction algorithm to extract shape features that were passed as input to SVM. SVM is exploited and trained to perform the greeting gesture recognition task to recognize two Nigerian tribe greeting postures. To confirm the robustness of the system, 20%, 25% and 30% of the dataset acquired from the preprocessed images were used to test the system. A recognition rate of 94% could be achieved when SVM is used, as shown by the result which invariably proves that the proposed method is efficient.

A Contactless Palm Vein Recognition System Based on Principle Line SIFT

2013 ◽  
Vol 347-350 ◽  
pp. 3469-3472 ◽  
Author(s):  
Wei Wu ◽  
Sen Lin ◽  
Hui Song
Keyword(s):  
Traditional Method ◽  
Recognition System ◽  
Scale Invariant ◽  
Limited Effect ◽  
Image Deformation ◽  
Invariant Feature ◽  
Feature Transform ◽  
Vein Recognition ◽  
Palm Vein ◽  
Scale Invariant Feature

Compared with the traditional method of contact collection, contactless acquisition is the mainstream and trend of palm vein recognition. However, this method may lead to image deformation caused by no parallel of the palm plane and the sensor plane. In order to improve the limited effect of Scale Invariant Feature Transform (SIFT) about this problem, a better method of palm vein recognition which based on principle line SIFT is proposed. Based on the self-built database, this method is compared with the SIFT and other typical palm vein recognition methods, the experimental results show that our system can achieve the best performance.

scholarly journals Design of Fractal Features-Based Partial Discharge Pattern Recognition using Multi Support Vector Machine Method

2018 ◽  
Vol 159 ◽  
pp. 02048
Author(s):  
Rahayu ◽  
G.T. Anuraga ◽  
H. Prasetia ◽  
Umar Khayam
Keyword(s):  
Pattern Recognition ◽  
Support Vector Machine ◽  
High Voltage ◽  
Partial Discharge ◽  
Diagnostic System ◽  
Diagnostic Technique ◽  
Recognition System ◽  
Support Vector ◽  
Machine Method ◽  
Pattern Recognition System

Partial Discharge (PD) is one of the causes of insulation deteriorisation mode and impacts on the reliability of high voltage equipment. Therefore, PD measurement is used for diagnostic technique of high voltage equipment. Diagnostic output of high voltage equipment contain information about PD type, PD cause, PD location and PD severity. after identification, a proper preventive maintenance pattern can be performed. Therefore PD pattern recognition system is very important on PD diagnostic system to recognize the PD pattern and determine the level of hazard that occurs in specimen object or high voltage equipment‥ In this paper, PD pattern recognition system is designed with fractal geometry approach and support vector machine (SVM) algorithm. The coding and programming of graphical user interface of the application is done. Each PD type and hazard level on various insulating materials (solid, liquid and gas) have the dimensions of the fractal and the lacunarity. The type of PD (void, corona) and its danger level (bad, fair and good) can be identified with the support vector machine (SVM)

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