Face Recognition using LBP Coefficient Vectors with SVM Classifier

2014 ◽  
Vol 9 (2) ◽  
pp. 96-100
Author(s):  
Sapna Vishwakarma ◽  
◽  
Krishan Kant Pathak
Keyword(s):  
Face Recognition ◽  
Svm Classifier

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 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

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.

scholarly journals Performance Evaluation of Face Recognition system by Concatenation of Spatial and Transformation Domain Features

2021 ◽  
Vol 13 (1) ◽  
pp. 47-60
Author(s):  
Raveendra K ◽  
◽  
Ravi J
Keyword(s):  
Feature Extraction ◽  
Performance Evaluation ◽  
Face Recognition ◽  
Frequency Domain ◽  
Recognition Rate ◽  
Curvelet Transform ◽  
Recognition System ◽  
Support Vector ◽  
Svm Classifier ◽  
Fast Discrete Curvelet Transform

Face biometric system is one of the successful applications of image processing. Person recognition using face is the challenging task since it involves identifying the 3D object from 2D object. The feature extraction plays a very important role in face recognition. Extraction of features both in spatial as well as frequency domain has more advantages than the features obtained from single domain alone. The proposed work achieves spatial domain feature extraction using Asymmetric Region Local Binary Pattern (ARLBP) and frequency domain feature extraction using Fast Discrete Curvelet Transform (FDCT). The obtained features are fused by concatenation and compared with trained set of features using different distance metrics and Support Vector Machine (SVM) classifier. The experiment is conducted for different face databases. It is shown that the proposed work yields 95.48% accuracy for FERET, 92.18% for L-space k, 76.55% for JAFFE and 81.44% for NIR database using SVM classifier. The results show that the proposed system provides better recognition rate for SVM classifier when compare to the other distance matrices. Further, the work is also compared with existing work for performance evaluation.

scholarly journals Face recognition using selected topographical features

2020 ◽  
Vol 10 (5) ◽  
pp. 4695
Author(s):  
Maitham Ali Naji ◽  
Ghalib Ahmed Salman ◽  
Muthna Jasim Fadhil
Keyword(s):  
Face Recognition ◽  
Hessian Matrix ◽  
Selection Method ◽  
Svm Classifier ◽  
Large Set ◽  
Features Selection ◽  
Main Concept ◽  
Linear Discriminant ◽  
Related Feature ◽  
Feature Selector

This paper represents a new features selection method to improve an existed feature type. Topographical (TGH) features provide large set of features by assigning each image pixel to the related feature depending on image gradient and Hessian matrix. Such type of features was handled by a proposed features selection method. A face recognition feature selector (FRFS) method is presented to inspect TGH features. FRFS depends in its main concept on linear discriminant analysis (LDA) technique, which is used in evaluating features efficiency. FRFS studies feature behavior over a dataset of images to determine the level of its performance. At the end, each feature is assigned to its related level of performance with different levels of performance over the whole image. Depending on a chosen threshold, the highest set of features is selected to be classified by SVM classifier

scholarly journals Extended Local Binary Pattern Features based Face Recognition using Multilevel SVM Classifier

2019 ◽  
Vol 8 (3) ◽  
pp. 4123-4128
Keyword(s):  
Face Recognition ◽  
Local Binary Pattern ◽  
Svm Classifier ◽  
Recognition Method ◽  
Face Area ◽  
Face Images ◽  
The Face ◽  
Zero Degree ◽  
The Individual ◽  
Biometric Trait

The Face recognition method is one of the authoritative biometric system in recognition methods to recognize the individual, because face is a distinctive biometric trait of an human being and it is the superior method of recognition. This paper proposes a novel Face recognition method by using extended LBP features. The pre-processing is carried out to extract the face area using viola-johns algorithm and all images are resized to 100x100. The LBP operator is applied on resized face images by rotating the each image by 15 degrees, i.e., at 7 degree left and 7 degree right and at zero degree to extract the feature vectors and final features are obtained by applying histogram technique. The SVM classifier is used for matching the database images with test images to measure the performance such as TSR, FAR, FRR & EER. The performance parameters are compared with existing algorithms for YALE and FERET database.

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