scholarly journals Using nonlocal filtering and feature extraction approaches in three-dimensional face recognition by Kinect

2018 ◽  
Vol 15 (4) ◽  
pp. 172988141878774 ◽  
Author(s):  
Shahram Mohammadi ◽  
Omid Gervei
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Recognition Rate ◽  
Three Dimensional ◽  
Component Analysis ◽  
Block Matching ◽  
Depth Image ◽  
Support Vector ◽  
Depth Images ◽  
Dimensional Face

To use low-cost depth sensors such as Kinect for three-dimensional face recognition with an acceptable rate of recognition, the challenges of filling up nonmeasured pixels and smoothing of noisy data need to be addressed. The main goal of this article is presenting solutions for aforementioned challenges as well as offering feature extraction methods to reach the highest level of accuracy in the presence of different facial expressions and occlusions. To use this method, a domestic database was created. First, the noisy pixels-called holes-of depth image is removed by solving multiple linear equations resulted from the values of the surrounding pixels of the holes. Then, bilateral and block matching 3-D filtering approaches, as representatives of local and nonlocal filtering approaches, are used for depth image smoothing. Curvelet transform as a well-known nonlocal feature extraction technique applied on both RGB and depth images. Two unsupervised dimension reduction techniques, namely, principal component analysis and independent component analysis, are used to reduce the dimension of extracted features. Finally, support vector machine is used for classification. Experimental results show a recognition rate of 90% for just depth images and 100% when combining RGB and depth data of a Kinect sensor which is much higher than other recently proposed algorithms.

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.

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 Research on an Improved MB-LBP 3D Face Recognition Method

2021 ◽  
pp. 306-314
Author(s):  
Liangliang Shi ◽  
◽  
Xia Wang ◽  
Yongliang Shen
Keyword(s):  
Face Recognition ◽  
Recognition Rate ◽  
Depth Image ◽  
Support Vector ◽  
3D Face Recognition ◽  
Recognition Method ◽  
Recognition Time ◽  
3D Face ◽  
Feature Information ◽  
Average Information

In order to improve the accuracy and speed of 3D face recognition, this paper proposes an improved MB-LBP 3D face recognition method. First, the MB-LBP algorithm is used to extract the features of 3D face depth image, then the average information entropy algorithm is used to extract the effective feature information of the image, and finallythe Support Vector Machine algorithm is used to identify the extracted effective information. The recognition rate on the Texas 3DFRD database is 96.88%, and the recognition time is 0.025s. The recognition rate in the self-made depth library is 96.36%, and the recognition time is 0.02s.It can be seen from the experimental results that the algorithm in this paper has better performance in terms of accuracy and speed.

scholarly journals Research on Face Recognition Algorithm Based on Multi-Class Support Vector Machine

2021 ◽  
Vol 2078 (1) ◽  
pp. 012049
Author(s):  
Liuxun Xue ◽  
Hui Li ◽  
Ping Wang ◽  
Zhiyang Lin ◽  
Huanyu Li ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Face Recognition ◽  
Facial Recognition ◽  
Recognition Rate ◽  
Support Vector ◽  
Machine Method ◽  
Feature Extraction Method ◽  
Main Research ◽  
Face Images

Abstract Facial recognition is one of the main research directions in the field of artificial intelligence and image processing. It has been widely used in identity authentication, video surveillance and biological detection. Because it is non-contact, natural, convenient and reliable, facial recognition has become a popular choice for biometric systems. The accuracy of facial recognition still needs to be improved, the main goal of this paper is to improve the accuracy of face recognition. Based on the support vector machine method, the focus is on the feature extraction and feature matching of face images. In view of the particularity of face images, the pre-processing of face images is studied. In this paper, grayscale normalization and geometric normalization pre-processing methods are used. In order to reduce the interference factors of the image as much as possible, the features are high-lighted, and the non-featured parts are weakened, this paper adopts the Histogram of Oriented Gradient feature extraction method. Then we proposed a new method based on SVM, which uses a one-to-many method to construct multiple SVM classifiers, selects the optimal parameters through repeated experiments, and selects ORL face database for testing. The recognition rate can reach about 98.5%.

Kernel Null Space Marginal Fisher Analysis for Face Recognition

2014 ◽  
Vol 889-890 ◽  
pp. 1065-1068
Author(s):  
Yu’e Lin ◽  
Xing Zhu Liang ◽  
Hua Ping Zhou
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Sample Size ◽  
High Performance ◽  
Null Space ◽  
Small Sample Size ◽  
Recognition Rate ◽  
Original Data ◽  
Small Sample ◽  
Neighborhood Structure

In the recent years, the feature extraction algorithms based on manifold learning, which attempt to project the original data into a lower dimensional feature space by preserving the local neighborhood structure, have drawn much attention. Among them, the Marginal Fisher Analysis (MFA) achieved high performance for face recognition. However, MFA suffers from the small sample size problems and is still a linear technique. This paper develops a new nonlinear feature extraction algorithm, called Kernel Null Space Marginal Fisher Analysis (KNSMFA). KNSMFA based on a new optimization criterion is presented, which means that all the discriminant vectors can be calculated in the null space of the within-class scatter. KNSMFA not only exploits the nonlinear features but also overcomes the small sample size problems. Experimental results on ORL database indicate that the proposed method achieves higher recognition rate than the MFA method and some existing kernel feature extraction algorithms.

A novel method for spacecraft electrical fault detection based on FCM clustering and WPSVM classification with PCA feature extraction

2016 ◽  
Vol 231 (1) ◽  
pp. 98-108 ◽  
Author(s):  
Ke Li ◽  
Yalei Wu ◽  
Shimin Song ◽  
Yi sun ◽  
Jun Wang ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Clustering Algorithm ◽  
High Efficiency ◽  
Computing Time ◽  
Principal Component ◽  
Component Analysis ◽  
Support Vector ◽  
Electrical Characteristics ◽  
Complex Signals ◽  
Fcm Clustering

The measurement of spacecraft electrical characteristics and multi-label classification issues are generally including a large amount of unlabeled test data processing, high-dimensional feature redundancy, time-consumed computation, and identification of slow rate. In this paper, a fuzzy c-means offline (FCM) clustering algorithm and the approximate weighted proximal support vector machine (WPSVM) online recognition approach have been proposed to reduce the feature size and improve the speed of classification of electrical characteristics in the spacecraft. In addition, the main component analysis for the complex signals based on the principal component feature extraction is used for the feature selection process. The data capture contribution approach by using thresholds is furthermore applied to resolve the selection problem of the principal component analysis (PCA), which effectively guarantees the validity and consistency of the data. Experimental results indicate that the proposed approach in this paper can obtain better fault diagnosis results of the spacecraft electrical characteristics’ data, improve the accuracy of identification, and shorten the computing time with high efficiency.

scholarly journals Persian Handwritten Digit Recognition Using Combination of Convolutional Neural Network and Support Vector Machine Methods

2020 ◽  
Vol 17 (4) ◽  
pp. 572-578
Author(s):  
Mohammad Parseh ◽  
Mohammad Rahmanimanesh ◽  
Parviz Keshavarzi
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Feature Extraction ◽  
Convolutional Neural Network ◽  
Recognition Rate ◽  
Support Vector ◽  
Svm Classifier ◽  
Handwritten Digit Recognition ◽  
Digit Recognition ◽  
Handwritten Digit

Persian handwritten digit recognition is one of the important topics of image processing which significantly considered by researchers due to its many applications. The most important challenges in Persian handwritten digit recognition is the existence of various patterns in Persian digit writing that makes the feature extraction step to be more complicated.Since the handcraft feature extraction methods are complicated processes and their performance level are not stable, most of the recent studies have concentrated on proposing a suitable method for automatic feature extraction. In this paper, an automatic method based on machine learning is proposed for high-level feature extraction from Persian digit images by using Convolutional Neural Network (CNN). After that, a non-linear multi-class Support Vector Machine (SVM) classifier is used for data classification instead of fully connected layer in final layer of CNN. The proposed method has been applied to HODA dataset and obtained 99.56% of recognition rate. Experimental results are comparable with previous state-of-the-art methods

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