scholarly journals Comparison between Fisher’s Ratio and Information Gain with SVM classifier for 3 levels of enthusiasm classification through face recognition

2021 ◽  
Vol 1752 (1) ◽  
pp. 012042
Author(s):  
Z Rustam ◽  
Andrea Laksmirani Kristina ◽  
Y Satria
Keyword(s):  
Face Recognition ◽  
Information Gain ◽  
Svm Classifier ◽  
Fisher’S Ratio

3D Face Recognition Based on Symbolic FDA Using SVM Classifier with Similarity and Dissimilarity Distance Measure

2017 ◽  
Vol 31 (04) ◽  
pp. 1756006 ◽  
Author(s):  
Manjunatha Hiremath ◽  
P. S. Hiremath
Keyword(s):  
Face Recognition ◽  
Distance Measure ◽  
Svm Classifier ◽  
Depth Information ◽  
3D Face Recognition ◽  
Emotional States ◽  
Range Images ◽  
Human Face ◽  
Biometric Technology ◽  
3D Face

Human face images are the basis not only for person recognition, but for also identifying other attributes like gender, age, ethnicity, and emotional states of a person. Therefore, face is an important biometric identifier in the law enforcement and human–computer interaction (HCI) systems. The 3D human face recognition is emerging as a significant biometric technology. Research interest into 3D face recognition has increased during recent years due to availability of improved 3D acquisition devices and processing algorithms. A 3D face image is represented by 3D meshes or range images which contain depth information. In this paper, the objective is to propose a new 3D face recognition method based on radon transform and symbolic factorial discriminant analysis using KNN and SVM classifier with similarity and dissimilarity measures, which are applied on 3D facial range images. The experimentation is done using three publicly available databases, namely, Bhosphorus, Texas and CASIA 3D face database. The experimental results demonstrate the effectiveness of the proposed method.

scholarly journals Ensemble swarm behaviour based feature selection and support vector machine classifier for chronic kidney disease prediction

2018 ◽  
Vol 7 (2.31) ◽  
pp. 190 ◽  
Author(s):  
S Belina V.J. Sara ◽  
K Kalaiselvi
Keyword(s):  
Chronic Kidney Disease ◽  
Support Vector Machine ◽  
Feature Selection ◽  
Kidney Disease ◽  
Information Gain ◽  
Clonal Selection ◽  
Prediction Algorithm ◽  
Support Vector ◽  
Svm Classifier ◽  
Classification Algorithms

Kidney Disease and kidney failure is the one of the complicated and challenging health issues regarding human health. Without having any symptoms few diseases are detected in later stages which results in dialysis. Advanced excavating technologies can always give various possibilities to deal with the situation by determining important realations and associations in drilling down health related data.   The prediction accuracy of classification algorithms depends upon appropriate Feature Selection (FS) algorithms decrease the number of features from collection of data. FS is the procedure of choosing the most relevant features, removing irrelevant features. To identify the Chronic Kidney Disease (CKD), Hybrid Wrapper and Filter based FS (HWFFS) algorithm is proposed to reduce the dimension of CKD dataset.   Filter based FS algorithm is performed based on the three major functions: Information Gain (IG), Correlation Based Feature Selection (CFS) and Consistency Based Subset Evaluation (CS) algorithms respectively. Wrapper based FS algorithm is performed based on the Enhanced Immune Clonal Selection (EICS) algorithm to choose most important features from the CKD dataset.  The results from these FS algorithms are combined with new HWFFS algorithm using classification threshold value.  Finally Support Vector Machine (SVM) based prediction algorithm be proposed in order to predict CKD and being evaluated on the MATLAB platform. The results demonstrated with the purpose of the SVM classifier by using HWFFS algorithm provides higher prediction rate in the diagnosis of CKD when compared to other classification algorithms.  

scholarly journals Face Recognition Across Gender Transformation Using SVM Classifier

2016 ◽  
Vol 24 ◽  
pp. 1366-1373 ◽  
Author(s):  
Archana Vijayan ◽  
Shyma Kareem ◽  
Jubilant J. Kizhakkethottam
Keyword(s):  
Face Recognition ◽  
Svm Classifier ◽  
Gender Transformation

Sentiment classification using hybrid feature selection and ensemble classifier

2021 ◽  
pp. 1-10 ◽  
Author(s):  
Achin Jain ◽  
Vanita Jain
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Information Gain ◽  
Statistical Significance ◽  
Sentiment Classification ◽  
Ensemble Classification ◽  
Support Vector ◽  
Svm Classifier ◽  
Feature Selection Technique ◽  
Restaurant Reviews

This paper presents a Hybrid Feature Selection Technique for Sentiment Classification. We have used a Genetic Algorithm and a combination of existing Feature Selection methods, namely: Information Gain (IG), CHI Square (CHI), and GINI Index (GINI). First, we have obtained features from three different selection approaches as mentioned above and then performed the UNION SET Operation to extract the reduced feature set. Then, Genetic Algorithm is applied to optimize the feature set further. This paper also presents an Ensemble Approach based on the error rate obtained different domain datasets. To test our proposed Hybrid Feature Selection and Ensemble Classification approach, we have considered four Support Vector Machine (SVM) classifier variants. We have used UCI ML Datasets of three domains namely: IMDB Movie Review, Amazon Product Review and Yelp Restaurant Reviews. The experimental results show that our proposed approach performed best in all three domain datasets. Further, we also presented T-Test for Statistical Significance between classifiers and comparison is also done based on Precision, Recall, F1-Score, AUC and model execution time.

An improved DFA based kernel ensemble learning machine using local feature representations for face recognition

2021 ◽  
pp. 1-14
Author(s):  
N Kavitha ◽  
K Ruba Soundar ◽  
T Sathis Kumar
Keyword(s):  
Face Recognition ◽  
Ensemble Learning ◽  
Information Gain ◽  
Recognition Task ◽  
Principal Component ◽  
Input Image ◽  
Kernel Principal Component Analysis ◽  
Face Images ◽  
The Face ◽  
Learning Machine

In recent years, the Face recognition task has been an active research area in computer vision and biometrics. Many feature extraction and classification algorithms are proposed to perform face recognition. However, the former usually suffer from the wide variations in face images, while the latter usually discard the local facial features, which are proven to be important for face recognition. In this paper, a novel framework based on merging the advantages of the Key points Local Binary/Tetra Pattern (KP-LTrP) and Improved Hough Transform (IHT) with the Improved DragonFly Algorithm-Kernel Ensemble Learning Machine (IDFA-KELM) is proposed to address the face recognition problem in unconstrained conditions. Initially, the face images are collected from the publicly available dataset. Then noises in the input image are removed by performing preprocessing using Adaptive Kuwahara filter (AKF). After preprocessing, the face from the preprocessed image is detected using the Tree-Structured Part Model (TSPM) structure. Then, features, such as KP-LTrP, and IHT are extracted from the detected face and the extracted feature is reduced using the Information gain based Kernel Principal Component Analysis (IG-KPCA) algorithm. Then, finally, these reduced features are inputted to IDFA-KELM for performing FR. The outcomes of the proposed method are examined and contrasted with the other existing techniques to confirm that the proposed IDFA-KELM detects human faces efficiently from the input images.

scholarly journals A Face Recognition System using Directional Binary Code Algorithm and Multi-SVM

2019 ◽  
Vol 8 (6S3) ◽  
pp. 1201-1208
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Binary Code ◽  
Recognition System ◽  
Extraction Methods ◽  
Recognition Algorithm ◽  
Svm Classifier ◽  
Feature Extraction Method ◽  
Face Recognition System ◽  
The Face

Over past few years, face recognition technology plays an important function in the development of biometric identifier with less time consuming and computational overhead. Many researchers were put their effort to develop face recognition algorithm involves three distinct steps such as detection, unique faceprint creation and finally verification. Traditional Local binary pattern based face recognition system slow down the recognition speed, high computational complexity and does not give the directional data of the picture. In order to overcome the above limitation, a novel face recognition system is proposed by employing the advantage of Directional Binary Code (DBC) feature extraction method. The face images features are extracted from DBC are generally smoother than other feature extraction methods. The images with blur creation, pose changes, and illumination is applied and stored in the database. For blur creation various filters such as Average filter, Gaussian filter and Motion filter are used. By using Directional Binary Code method, the face is detected and extracted. Then the same algorithm is used for input images and with help of Multi-SVM classifier multiple images in the database is compared and shows the matched images. Finally, simulation result shows the implemented results in term of its recognition speed and computation complexity.

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.

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