scholarly journals Vehicle Starter using Face Recognition

2020 ◽  
Vol 9 (5) ◽  
pp. 711-713
Keyword(s):  
Support Vector Machine ◽  
Face Recognition ◽  
Support Vector ◽  
Svm Classifier ◽  
Outward Appearance ◽  
Speeded Up Robust Features ◽  
The Face ◽  
Arduino Microcontroller ◽  
Relative Change ◽  
Fundamental Requirement

Face acknowledgment is an interesting exploration subject as of late. The scientists proposed different strategies. The factors are similar to an assortment of lighting up, outward appearance, leveling, and perspective turn of events influences the precision of the face affirmation procedure. The fundamental requirement is the separation of the facial picture and the SURF (Speeded up Robust Features). Notwithstanding that they are additionally halfway invariable to brightening and relative change. This undertaking recommends a facial acknowledgment procedure utilizing SURF highlights and Support Vector Machine (SVM) classifier. The outcomes demonstrate that the proposed technique can prompt high acknowledgment productivity. The proposed framework is applied to vehicle get to control by interfacing the Arduino microcontroller board with PC.

Face Recognition with Single Sample Based on Candide-3 Reconstruction Model

2013 ◽  
Vol 380-384 ◽  
pp. 3623-3628 ◽  
Author(s):  
Nan Deng ◽  
Ya Bo Pei ◽  
Zheng Guang Xu
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Face Recognition ◽  
Recognition Rate ◽  
Single Sample ◽  
Support Vector ◽  
Training Samples ◽  
Virtual Images ◽  
The Face ◽  
Reconstruction Model

In this study, we present a method for virtual images generation based on Candide-3 model to increase the number of training samples for the face recognition with single sample, where the Principle Component Analysis is used for feature extraction and the test samples are classified by the method of Support Vector Machine (SVM). Experimental results on from the YaleB and ORL databases show that the recognition rate of the face recognition with single sample can be improved by the proposed method.

Analysis of age invariant face recognition using quadratic support vector machine-principal component analysis

2021 ◽  
pp. 1-15
Author(s):  
Ashutosh Dhamija ◽  
R. B. Dubey
Keyword(s):  
Principal Component Analysis ◽  
Support Vector Machine ◽  
Face Recognition ◽  
Principal Component ◽  
Component Analysis ◽  
Support Vector ◽  
Identity Recognition ◽  
The Face ◽  
A New Technique ◽  
Age Range

Face recognition is one of the most challenging and demanding field, since aging affects the shape and structure of the face. Age invariant face recognition is a relatively new area in face recognition studies, which in real-world implementations recently gained considerable interest due to its huge potential and relevance. The Age invariant face recognition, however, is still evolving and evolving, providing substantial potential for further study and progress inaccuracy. Major issues with the age invariant face recognition involve major variations in appearance, texture, and facial features and discrepancies in position and illumination. These problems restrict the age invariant face recognition systems developed and intensify identity recognition tasks. To address this problem, a new technique Quadratic Support Vector Machine- Principal Component Analysis (QSVM-PCA) is introduced. Experimental results suggest that our QSVM-PCA achieved better results especially when the age range is larger than other existing techniques of face-aging dataset of FGNET. The maximum accuracy achieved by demonstrated methodology is 98.87%.

Face and age recognition using three-dimensional discrete wavelet transform and rotational local binary pattern with radial basis function support vector machine method

2021 ◽  
pp. 002072092098848
Author(s):  
Ajit Singh ◽  
Chander Kant
Keyword(s):  
Support Vector Machine ◽  
Face Recognition ◽  
Wavelet Transform ◽  
Basis Function ◽  
Detection Rate ◽  
Local Binary Pattern ◽  
Face Image ◽  
Support Vector ◽  
Discrete Wavelet ◽  
The Face

Interest in facial recognition hypotheses and algorithms has grown steadily over the last few decades. Video monitoring, criminal identification, building access control, and unmanned and autonomous vehicles are only a few examples of concrete applications that are becoming increasingly attractive to industry. Various techniques are being developed, including local, holistic, and hybrid approaches, which use only a few face image characteristics or the entire facial features to provide a face image description. Many methods have good results, if there are sufficiently representative training samples per person, in the face recognition system. Facial part finding and extraction show the utmost vital role in face and age recognition. In this research work a new algorithm is proposed for Face and Age Recognition (FAR) by using Discrete Wavelet Transform (DWT), Radial Basis Function Support Vector Machine (RBF-SVM) classifier, and Rotational Local Binary Pattern (RLBP). RLBP is utilized for the selection and extraction of features from the face image. In this algorithm, extract the face component like Nose, Mouth, Left and Right eye. In the preprocessing stage median filter is used to remove noises from the face image. By using this, there is an improvement in the feature extraction procedure. In pattern recognition, a basic errand is finding a picture from the picture parts. For the implementation of results FG-NET ((Face and Gesture Recognition Network) and AT&T datasets are used. The detection rate of face recognition has reached up to 92–98% and the detection rate for age recognition is 87%. The proposed algorithm is compared with SVM shows better over previous algorithms and also estimate the value of accuracy.

scholarly journals Analog Circuit Fault Diagnosis Based on Support Vector Machine Classifier and Fuzzy Feature Selection

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1496
Author(s):  
Hao Liang ◽  
Yiman Zhu ◽  
Dongyang Zhang ◽  
Le Chang ◽  
Yuming Lu ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Mutual Information ◽  
Analog Circuit ◽  
Fault Classification ◽  
Support Vector ◽  
Svm Classifier ◽  
Fault Parameters ◽  
Diagnosis Method ◽  
Circuit Fault Diagnosis

In analog circuit, the component parameters have tolerances and the fault component parameters present a wide distribution, which brings obstacle to classification diagnosis. To tackle this problem, this article proposes a soft fault diagnosis method combining the improved barnacles mating optimizer(BMO) algorithm with the support vector machine (SVM) classifier, which can achieve the minimum redundancy and maximum relevance for feature dimension reduction with fuzzy mutual information. To be concrete, first, the improved barnacles mating optimizer algorithm is used to optimize the parameters for learning and classification. We adopt six test functions that are on three data sets from the University of California, Irvine (UCI) machine learning repository to test the performance of SVM classifier with five different optimization algorithms. The results show that the SVM classifier combined with the improved barnacles mating optimizer algorithm is characterized with high accuracy in classification. Second, fuzzy mutual information, enhanced minimum redundancy, and maximum relevance principle are applied to reduce the dimension of the feature vector. Finally, a circuit experiment is carried out to verify that the proposed method can achieve fault classification effectively when the fault parameters are both fixed and distributed. The accuracy of the proposed fault diagnosis method is 92.9% when the fault parameters are distributed, which is 1.8% higher than other classifiers on average. When the fault parameters are fixed, the accuracy rate is 99.07%, which is 0.7% higher than other classifiers on average.

scholarly journals Deeply Learned Pose Invariant Image Analysis with Applications in 3D Face Recognition

2019 ◽  
Vol 2019 ◽  
pp. 1-21 ◽  
Author(s):  
Naeem Ratyal ◽  
Imtiaz Ahmad Taj ◽  
Muhammad Sajid ◽  
Anzar Mahmood ◽  
Sohail Razzaq ◽  
...  
Keyword(s):  
Face Recognition ◽  
Superior Performance ◽  
Support Vector ◽  
Complex Nature ◽  
Alignment Algorithm ◽  
3D Face Recognition ◽  
3D Face ◽  
Norm Minimization ◽  
Wide Range ◽  
The Face

Face recognition aims to establish the identity of a person based on facial characteristics and is a challenging problem due to complex nature of the facial manifold. A wide range of face recognition applications are based on classification techniques and a class label is assigned to the test image that belongs to the unknown class. In this paper, a pose invariant deeply learned multiview 3D face recognition approach is proposed and aims to address two problems: face alignment and face recognition through identification and verification setups. The proposed alignment algorithm is capable of handling frontal as well as profile face images. It employs a nose tip heuristic based pose learning approach to estimate acquisition pose of the face followed by coarse to fine nose tip alignment using L2 norm minimization. The whole face is then aligned through transformation using knowledge learned from nose tip alignment. Inspired by the intrinsic facial symmetry of the Left Half Face (LHF) and Right Half Face (RHF), Deeply learned (d) Multi-View Average Half Face (d-MVAHF) features are employed for face identification using deep convolutional neural network (dCNN). For face verification d-MVAHF-Support Vector Machine (d-MVAHF-SVM) approach is employed. The performance of the proposed methodology is demonstrated through extensive experiments performed on four databases: GavabDB, Bosphorus, UMB-DB, and FRGC v2.0. The results show that the proposed approach yields superior performance as compared to existing state-of-the-art methods.

scholarly journals A Roller Bearing Fault Diagnosis Method Based on LCD Energy Entropy and ACROA-SVM

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
HungLinh Ao ◽  
Junsheng Cheng ◽  
Kenli Li ◽  
Tung Khac Truong
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Roller Bearing ◽  
Local Characteristic ◽  
Support Vector ◽  
Svm Classifier ◽  
Outer Race ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Energy Entropy

This study investigates a novel method for roller bearing fault diagnosis based on local characteristic-scale decomposition (LCD) energy entropy, together with a support vector machine designed using an Artificial Chemical Reaction Optimisation Algorithm, referred to as an ACROA-SVM. First, the original acceleration vibration signals are decomposed into intrinsic scale components (ISCs). Second, the concept of LCD energy entropy is introduced. Third, the energy features extracted from a number of ISCs that contain the most dominant fault information serve as input vectors for the support vector machine classifier. Finally, the ACROA-SVM classifier is proposed to recognize the faulty roller bearing pattern. The analysis of roller bearing signals with inner-race and outer-race faults shows that the diagnostic approach based on the ACROA-SVM and using LCD to extract the energy levels of the various frequency bands as features can identify roller bearing fault patterns accurately and effectively. The proposed method is superior to approaches based on Empirical Mode Decomposition method and requires less time.

scholarly journals Aero-Engine Fault Diagnosis Using Improved Local Discriminant Bases and Support Vector Machine

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Jianwei Cui ◽  
Mengxiao Shan ◽  
Ruqiang Yan ◽  
Yahui Wu
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Wavelet Packet ◽  
Energy Difference ◽  
Test System ◽  
Support Vector ◽  
Svm Classifier ◽  
Aero Engine ◽  
Aero Engines ◽  
Optimal Set

This paper presents an effective approach for aero-engine fault diagnosis with focus on rub-impact, through combination of improved local discriminant bases (LDB) with support vector machine (SVM). The improved LDB algorithm, using both the normalized energy difference and the relative entropy as quantification measures, is applied to choose the optimal set of orthogonal subspaces for wavelet packet transform- (WPT-) based signal decomposition. Then two optimal sets of orthogonal subspaces have been obtained and the energy features extracted from those subspaces appearing in both sets will be selected as input to a SVM classifier to diagnose aero-engine faults. Experiment studies conducted on an aero-engine rub-impact test system have verified the effectiveness of the proposed approach for classifying working conditions of aero-engines.

Shape-restricted support vector machine (SR-SVM): a SVM classifier taking supplementary shape information of input

2021 ◽  
Vol 40 (1) ◽  
pp. 1481-1494
Author(s):  
Geng Deng ◽  
Yaoguo Xie ◽  
Xindong Wang ◽  
Qiang Fu
Keyword(s):  
Support Vector Machine ◽  
Classification Performance ◽  
Research Literature ◽  
Support Vector ◽  
Svm Classifier ◽  
Classification Problems ◽  
Active Set ◽  
Shape Information ◽  
Convex Optimization Problem ◽  
Shape Restrictions

Many classification problems contain shape information from input features, such as monotonic, convex, and concave. In this research, we propose a new classifier, called Shape-Restricted Support Vector Machine (SR-SVM), which takes the component-wise shape information to enhance classification accuracy. There exists vast research literature on monotonic classification covering monotonic or ordinal shapes. Our proposed classifier extends to handle convex and concave types of features, and combinations of these types. While standard SVM uses linear separating hyperplanes, our novel SR-SVM essentially constructs non-parametric and nonlinear separating planes subject to component-wise shape restrictions. We formulate SR-SVM classifier as a convex optimization problem and solve it using an active-set algorithm. The approach applies basis function expansions on the input and effectively utilizes the standard SVM solver. We illustrate our methodology using simulation and real world examples, and show that SR-SVM improves the classification performance with additional shape information of input.

Sign in / Sign up

Export Citation Format

Share Document