scholarly journals Effective Usage of Support Vector Machine in Face Detection

2020 ◽  
Vol 9 (3) ◽  
pp. 1336-1340
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Facial Recognition ◽  
Recognition System ◽  
Support Vector ◽  
Biometric Technology ◽  
Multimedia Contents ◽  
The Past ◽  
Vector Machines ◽  
Facial Recognition System

With the rapid growth in Technology in terms of multimedia contents such as Biometrics, Facial recognition etc. Facial detection got much attention over the past few years. Face recognition describes a biometric technology that attempts to establish an identity. In this paper, I would like to review about a facial recognition system using machine learning especially, using support vector machines. In any case, point of this exploration is to give extensive writing survey over face acknowledgment alongside its applications. Furthermore, after top to bottom conversation, a portion of the significant discoveries are given in end.

scholarly journals Research on Parallel Support Vector Machine Based on Spark Big Data Platform

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yao Huimin
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Big Data ◽  
Support Vector Machines ◽  
Cross Validation ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Lambda Architecture ◽  
Vector Machines ◽  
Data Platform

With the development of cloud computing and distributed cluster technology, the concept of big data has been expanded and extended in terms of capacity and value, and machine learning technology has also received unprecedented attention in recent years. Traditional machine learning algorithms cannot solve the problem of effective parallelization, so a parallelization support vector machine based on Spark big data platform is proposed. Firstly, the big data platform is designed with Lambda architecture, which is divided into three layers: Batch Layer, Serving Layer, and Speed Layer. Secondly, in order to improve the training efficiency of support vector machines on large-scale data, when merging two support vector machines, the “special points” other than support vectors are considered, that is, the points where the nonsupport vectors in one subset violate the training results of the other subset, and a cross-validation merging algorithm is proposed. Then, a parallelized support vector machine based on cross-validation is proposed, and the parallelization process of the support vector machine is realized on the Spark platform. Finally, experiments on different datasets verify the effectiveness and stability of the proposed method. Experimental results show that the proposed parallelized support vector machine has outstanding performance in speed-up ratio, training time, and prediction accuracy.

scholarly journals Efficient Face Recognition System for Operating in Unconstrained Environments

2021 ◽  
Vol 7 (9) ◽  
pp. 161
Author(s):  
Alejandra Sarahi Sanchez-Moreno ◽  
Jesus Olivares-Mercado ◽  
Aldo Hernandez-Suarez ◽  
Karina Toscano-Medina ◽  
Gabriel Sanchez-Perez ◽  
...  
Keyword(s):  
Real Time ◽  
Face Detection ◽  
High Speed ◽  
Learning Algorithm ◽  
Facial Recognition ◽  
Processing System ◽  
Recognition System ◽  
Support Vector ◽  
The Face ◽  
Facial Recognition System

Facial recognition is fundamental for a wide variety of security systems operating in real-time applications. Recently, several deep neural networks algorithms have been developed to achieve state-of-the-art performance on this task. The present work was conceived due to the need for an efficient and low-cost processing system, so a real-time facial recognition system was proposed using a combination of deep learning algorithms like FaceNet and some traditional classifiers like SVM, KNN, and RF using moderate hardware to operate in an unconstrained environment. Generally, a facial recognition system involves two main tasks: face detection and recognition. The proposed scheme uses the YOLO-Face method for the face detection task which is a high-speed real-time detector based on YOLOv3, while, for the recognition stage, a combination of FaceNet with a supervised learning algorithm, such as the support vector machine (SVM), is proposed for classification. Extensive experiments on unconstrained datasets demonstrate that YOLO-Face provides better performance when the face under an analysis presents partial occlusion and pose variations; besides that, it can detect small faces. The face detector was able to achieve an accuracy of over 89.6% using the Honda/UCSD dataset which runs at 26 FPS with darknet-53 to VGA-resolution images for classification tasks. The experimental results have demonstrated that the FaceNet+SVM model was able to achieve an accuracy of 99.7% using the LFW dataset. On the same dataset, FaceNet+KNN and FaceNet+RF achieve 99.5% and 85.1%, respectively; on the other hand, the FaceNet was able to achieve 99.6%. Finally, the proposed system provides a recognition accuracy of 99.1% and 49 ms runtime when both the face detection and classifications stages operate together.

Support Vector Machines Illuminated

2011 ◽  
pp. 1071-1076 ◽  
Author(s):  
David R. Musicant
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Support Vector Machine ◽  
Support Vector Machines ◽  
Data Storage ◽  
Research Data ◽  
Support Vector ◽  
Machine Learning Technique ◽  
Vector Machines ◽  
A Current

In recent years, massive quantities of business and research data have been collected and stored, partly due to the plummeting cost of data storage. Much interest has therefore arisen in how to mine this data to provide useful information. Data mining as a discipline shares much in common with machine learning and statistics, as all of these endeavors aim to make predictions about data as well as to better understand the patterns that can be found in a particular dataset. The support vector machine (SVM) is a current machine learning technique that performs quite well in solving common data mining problems.

scholarly journals The Sparse Learning of The Support Vector Machine

2021 ◽  
Vol 2078 (1) ◽  
pp. 012006
Author(s):  
Lipeng Cui ◽  
Jie Shen ◽  
Song Yao
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Support Vector Machines ◽  
Sparse Learning ◽  
Support Vector ◽  
Research Directions ◽  
Sparse Model ◽  
Vector Machines ◽  
Fitting Problem

Abstract The sparse model plays an important role in many aeras, such as in the machine learning, image processing and signal processing. The sparse model has the ability of variable selection, so they can solve the over-fitting problem. The sparse model can be introduced into the field of support vector machine in order to get classification of the labels and sparsity of the variables simultaneously. This paper summarizes various sparse support vector machines. Finally, we revealed the research directions of the sparse support vector machines in the future.

scholarly journals Efficient Attendance Management System Based on Facial Recognition

2018 ◽  
Vol 7 (3.12) ◽  
pp. 565 ◽  
Author(s):  
Setu Choudhary ◽  
Aayush Kakaji ◽  
Kusuma Pranay ◽  
P Prabhu
Keyword(s):  
Computer Vision ◽  
Support Vector Machine ◽  
Face Recognition ◽  
Data Base ◽  
Management System ◽  
Facial Recognition ◽  
Recognition System ◽  
Support Vector ◽  
Time Consumption ◽  
Face Recognition System

A Face recognition system is an application of computer vision which is capable of performing two major tasks identifying and verifying a person from  given data base. The objective of this paper is to design an effective attendance system which is based on facial recognition and intend to reduce the manual efforts of the teacher. In the conventional attendance system there are several issue like fake attendance, time consumption, manipulation of attendance. The algorithm used is named fisher face algorithm, which is already in use but it gives an accuracy of 5-6% and the amount of faces it can detect is comparatively less, Here we intend to use fisher face algorithm with the help of support vector machine(SVM). The system is trained with database faces.  The data gets updated in the portal which is accessed by the faculty and the students. This paper is a speculative model of attendance management system using facial recognition.

scholarly journals Applying support-vector machine learning algorithms toward predicting host–guest interactions with cucurbit[7]uril

2020 ◽  
Vol 22 (26) ◽  
pp. 14976-14982
Author(s):  
Anthony Tabet ◽  
Thomas Gebhart ◽  
Guanglu Wu ◽  
Charlie Readman ◽  
Merrick Pierson Smela ◽  
...  
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Support Vector Machines ◽  
Small Molecules ◽  
Binding Constant ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Equilibrium Binding ◽  
Vector Machines ◽  
Equilibrium Binding Constant

We evaluate the ability of support-vector machines to predict the equilibrium binding constant of small molecules to cucurbit[7]uril.

scholarly journals KLASIFIKASI HABITAT BENTIK BERBASIS OBJEK DENGAN ALGORITMA SUPPORT VECTOR MACHINES DAN DECISION TREE MENGGUNAKAN CITRA MULTISPEKTRAL SPOT-7 DI PULAU HARAPAN DAN PULAU KELAPA

2018 ◽  
Vol 10 (1) ◽  
pp. 123-134 ◽  
Author(s):  
Nico Wantona Prabowo ◽  
Vincentius P. Siregar ◽  
Syamsul Bahri Agus
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Support Vector Machines ◽  
Decision Tree ◽  
Support Vector ◽  
Vector Machines ◽  
Multiresolution Segmentation

Teknik klasifikasi berbasis objek dengan algoritma machine learning SVM untuk citra resolusi tinggi di Indonesia sampai saat ini masih terbatas khususnya untuk pemetaan terumbu karang, oleh karena itu diperlukan kajian lebih lanjut mengenai perbandingan metode maupun penerapan algoritma sebagai alternatif dari proses klasifikasi. Penelitian ini bertujuan memetakan habitat bentik berdasarkan klasifikasi menggunakan metode OBIA dengan algoritma support vector machine dan decision tree di Pulau Harapan dan Kelapa. Segmentasi dilakukan menggunakan algoritma multiresolution segmentation dengan faktor skala 15. Metode OBIA diterapkan pada citra terkoreksi atmosfer dengan skema klasifikasi habitat bentik yang telah ditentukan sebelumnya. Akurasi keseluruhan dari penerapan algoritma SVM dan DT masing-masing sebesar 75,11% dan 60,34%. Analisis nilai Z statistik yang diperoleh dari penerapan dua algoritma yang digunakan yakni sebesar 2,23, dimana nilai ini menunjukkan bahwa klasifikasi dengan algoritma SVM berbeda nyata dengan hasil dari penggunaan algoritma DT.  

A Proposed Methodology for Online Implementation of a Support Vector Machine With Applications in Power Transformers Event Classification

2020 ◽  
Author(s):  
Hagi Jakobson Dantas da Costa ◽  
Bruna Liberato Souza ◽  
Lucas Dantas Simões ◽  
Matheus Aires ◽  
Rodrigo Prado Medeiros ◽  
...  
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Support Vector Machine Classifier ◽  
Hardware Implementation ◽  
Power Transformer ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Vector Machines ◽  
Online Implementation ◽  
Fast Classification

The constant evolution of resources in computational processing and machine learning algorithms, combined with the increasing complexity of embedded systems, made the hardware implementation of machine learning models more viable. This paper proposes a methodology for online implementation of a support vector machine classifier through the development of a simple, concise, and easily adapted algorithm for data classification. The system was validated through the development of an application that classifies disturbances in a power transformer, followed by a comparison with the results obtained with the Library for Support Vector Machines (LIBSVM). Besides the very similar results when compared with the LIBSVM, the proposed methodology achieved high overall accuracy and fast classification time.

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.

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