scholarly journals Face Recognition by Using 2D Orthogonal Subspace Projections

2021 ◽  
Vol 38 (1) ◽  
pp. 51-60
Author(s):  
Semih Ergin ◽  
Sahin Isik ◽  
Mehmet Bilginer Gulmezoglu
Keyword(s):  
Face Recognition ◽  
Nearest Neighbor ◽  
Recognition System ◽  
Experimental Results ◽  
Support Vector ◽  
Classification Problems ◽  
K Nearest Neighbor ◽  
The Face ◽  
Low Dimensional ◽  
Subspace Projections

In this paper, the implementations and comparison of some classifiers along with 2D subspace projection approaches have been carried out for the face recognition problem. For this purpose, the well-known classifiers such as K-Nearest Neighbor (K-NN), Common Matrix Approach (CMA), Support Vector Machine (SVM) and Convolutional Neural Network (CNN) are conducted on low dimensional face representations that are determined from 2DPCA-, 2DSVD- and 2DFDA approaches. CMA, which is a 2D version of the Common Vector Approach (CVA), finds a common matrix for each face class. From the experimental results, we have observed that the SVM presents a dominant performance in general. When overall results of all datasets are considered, CMA is slightly superior to others in case of 2DPCA- and 2DSVD-based features matrices of the AR dataset. On the other side, CNN is better than other classifiers when it comes to develop a face recognition system based on original face samples and 2DPCA-based feature matrices of the Yale dataset. The experimental results indicate that use of these feature matrices with CMA, SVM, and CNN in classification problems is more advantageous than the use of original pixel matrices in the sense of both processing time and memory requirement.

scholarly journals Analysis of Different M-Band Wavelet Filters for Face Recognition using Nearest Neighbor Classifier

2018 ◽  
Vol 12 (2) ◽  
pp. 824
Author(s):  
C Hemalatha ◽  
E Logashanmugam
Keyword(s):  
Face Recognition ◽  
Iris Recognition ◽  
Nearest Neighbor ◽  
Performance Metrics ◽  
Recognition System ◽  
K Nearest Neighbor ◽  
Wavelet Filters ◽  
Knn Classifier ◽  
Face Recognition System ◽  
The Face

<span>Face recognition system is one of the most interesting studied topics in computer vision for past two decades. Among the other popular biometrics such as the retina, fingerprint, and iris recognition systems, the face recognition is capable of recognizing the uncooperative samples in a non-intrusive manner. Also, it can be applied to many applications of surveillance security, forensics, border control, digital entertainment where face recognition is used in most. In the proposed system an automatic face recognition system is discussed. The proposed recognition system is based on the Dual-Tree M-Band Wavelet Transform (DTMBWT) transform algorithm and features obtained by varying the different filter in the DTMBWT transform. Then the different filter features are classified by means of the K-Nearest Neighbor (KNN) classifier for recognizing the face correctly. The implementation of the system is done by using the ORL face image database, and the performance metrics are calculated.</span>

Enhanced Face Recognition System

2019 ◽  
Vol 8 (1) ◽  
pp. 49-62
Author(s):  
Vinodpuri Rampuri Gosavi ◽  
Anil Kishanrao Deshmane ◽  
Ganesh Shahuba Sable
Keyword(s):  
Face Recognition ◽  
Nearest Neighbor ◽  
Focal Point ◽  
Random Noise ◽  
Recognition System ◽  
K Nearest Neighbor ◽  
Face Analysis ◽  
Face Recognition System ◽  
The Face ◽  
Common Application

Image processing has enormous applications and bio-metrics is one of them that has become a focal point for researchers, as well as for developers. The most common application of bio-metrics is the face analysis. The face analysis is an efficient method to detect and verify the faces of people. In this research article we have the proposed techniques are CRC and KNN. Generally, CRC (Collaboration representation based classification) relies on the collaboration among various classes to represent an image sample. KNN (K-Nearest Neighbor) it is a category of classification approach that utilized to access regression purposes. The experiment is performed on the Yale database and the results are acquired from the simulation tool MATLAB. The performance parameters are accurate, processing time, random noise and random occlusion. A comparison of performance is described and it is proven that the proposed method results give the enhancement in the overall performance of face recognition and accuracy value is 99%.

Identification of a Person From Live Video Streaming Using Machine Learning in the Internet of Things (IoT)

2021 ◽  
Vol 12 (3) ◽  
pp. 44-59
Author(s):  
Sana Zeba ◽  
Mohammad Amjad
Keyword(s):  
Machine Learning ◽  
Face Recognition ◽  
Video Streaming ◽  
Nearest Neighbor ◽  
Block Diagram ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Live Video ◽  
Live Video Streaming ◽  
The Face

In this paper, the authors develop an efficient face recognition algorithm from images or live video streaming for IoT systems based on K-nearest neighbor and support vector machine learning to recognize the person from the local database and extract the features of the face. Because of the complexity of the conditions, there might be some factors of facing errors like the size; the angle; the distance from the ear, nose, and eyes; etc. This sustainable machine learning-based IoT system is designed for sovereign face recognition with features extraction with improved accuracy near about 96%. The experimental study is done to test the performance of the face recognition in the changes of number of persons in video or images. Finally, this manuscript recognized persons from live video or images with accuracy approximately 96% by using the SVM and KNN classifiers and discussed with the block diagram and proposed algorithm.

FACE RECOGNITION USING CURVELET-BASED TWO-DIMENSIONAL PRINCIPLE COMPONENT ANALYSIS

2012 ◽  
Vol 26 (03) ◽  
pp. 1256009 ◽  
Author(s):  
YAN ZHANG ◽  
BIN YU ◽  
HAI-MING GU
Keyword(s):  
Face Recognition ◽  
Principle Component Analysis ◽  
Nearest Neighbor ◽  
Component Analysis ◽  
Recognition System ◽  
Support Vector ◽  
Svm Classifier ◽  
Two Dimensional ◽  
K Nearest Neighbor ◽  
Principle Component

The task of face recognition has been actively researched in recent years because of its many applications in various domains. This paper presents a robust face recognition system using curvelet-based two-dimensional principle component analysis (2D PCA) to address the problem of human face recognition from still images. 2D PCA has advantages over PCA in evaluating the covariance matrix accurately and time complexity. Inspired by the attractive attributes of curvelets in catching the edge singularities with very few coefficients in a non-adaptive manner, we introduce the scheme of decomposing images into curvelet subbands and applying 2D PCA to create a representative feature set. Experiments were designed with different implementations of each module using standard testing database. We experimented with changing the illumination normalization procedure; comparing the baseline PCA-based method with the proposed scheme; studying effects on algorithm performance of k-nearest neighbor (kNN) classifier and Support Vector Machine (SVM) classifier in the classification process; also we experimented with different databases such as FERET, etc. High accuracy rate were achieved by the proposed scheme through a comparative study.

Recognition of 3D Objects from 2D Views Features

2015 ◽  
Vol 13 (2) ◽  
pp. 50-58
Author(s):  
R. Khadim ◽  
R. El Ayachi ◽  
Mohamed Fakir
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Nearest Neighbor ◽  
Color Image ◽  
Recognition Rate ◽  
Experimental Results ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
3D Objects ◽  
Color Descriptor

This paper focuses on the recognition of 3D objects using 2D attributes. In order to increase the recognition rate, the present an hybridization of three approaches to calculate the attributes of color image, this hybridization based on the combination of Zernike moments, Gist descriptors and color descriptor (statistical moments). In the classification phase, three methods are adopted: Neural Network (NN), Support Vector Machine (SVM), and k-nearest neighbor (KNN). The database COIL-100 is used in the experimental results.

Performance Analysis of Pose Invariant Face Recognition Approaches in Unconstrained Environments

2015 ◽  
Vol 5 (1) ◽  
pp. 66-81 ◽  
Author(s):  
M. Parisa Beham ◽  
S. M. Mansoor Roomi ◽  
J. Alageshan ◽  
V. Kapileshwaran
Keyword(s):  
Face Recognition ◽  
Nearest Neighbor ◽  
State Of The Art ◽  
Recognition Algorithm ◽  
K Nearest Neighbor ◽  
Research Issues ◽  
Histograms Of Oriented Gradients ◽  
Recognition Phase ◽  
The Face ◽  
Pose Variation

Face recognition and authentication are two significant and dynamic research issues in computer vision applications. There are many factors that should be accounted for face recognition; among them pose variation is a major challenge which severely influence in the performance of face recognition. In order to improve the performance, several research methods have been developed to perform the face recognition process with pose invariant conditions in constrained and unconstrained environments. In this paper, the authors analyzed the performance of a popular texture descriptors viz., Local Binary Pattern, Local Derivative Pattern and Histograms of Oriented Gradients for pose invariant problem. State of the art preprocessing techniques such as Discrete Cosine Transform, Difference of Gaussian, Multi Scale Retinex and Gradient face have also been applied before feature extraction. In the recognition phase K- nearest neighbor classifier is used to accomplish the classification task. To evaluate the efficiency of pose invariant face recognition algorithm three publicly available databases viz. UMIST, ORL and LFW datasets have been used. The above said databases have very wide pose variations and it is proved that the state of the art method is efficient only in constrained situations.

scholarly journals Smartphone assist deep neural network model to recognize the high-quality tea using leaf maturity and its effect on leaf chlorophyll

2021 ◽  
Vol 13 (4) ◽  
pp. 1249-1255
Author(s):  
Utpal Barman ◽  
Ridip Dev Choudhury ◽  
Bipul Kumar Talukdar ◽  
George Bhokta ◽  
Sahrul Alom Choudhari ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Nearest Neighbor ◽  
Recognition System ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Tea Leaves ◽  
High Quality ◽  
Leaf Maturity ◽  
Tea Leaf

Immature and tender tea leaves always produce high-quality tea than mature tea leaves. Depending on the maturity and age of the leaf, the colour and texture of the tea leaf are different. The photosynthesis capacity of the tea leaf also changes with the change of leaf maturity. Though the tea farmer plucks, classifies, and recognizes the best tea leaves (immature and tender) by viewing the visual symptoms and position of the leaves, the method is not authentic all time and leads to the overall degradation of the tea quality. The present study presents a smartphone assist tea leaf recognition system by analyzing the colour and texture properties of the tea leaf. The six different colour features and 4 Haralick texture features were extracted in the colour and grey domain of the leaf images. Three types of tea leaves, i.e., mature, immature, and tender, were classified using Deep Neural Network (DNN) with ADAM (Adaptive Moment Estimation) optimizer. With an accuracy of 97%, the DNN outperformed the Support Vector Machine (SVM) and K Nearest Neighbor (KNN). The SVM and KNN reported a total of 94.42% and 95.53% accuracy, respectively. The investigated system using DNN with an average precision and recall value of 98.67 and 98.34, respectively, may detect and classify the tea leaf maturity status. The system also can be used in AI-based tea plucking robotic systems or machines.

scholarly journals Optimized Kernel Extreme Learning Machine for Myoelectric Pattern Recognition

2018 ◽  
Vol 8 (1) ◽  
pp. 483
Author(s):  
Khairul Anam ◽  
Adel Al-Jumaily
Keyword(s):  
Pattern Recognition ◽  
Extreme Learning Machine ◽  
Healthy Subjects ◽  
Nearest Neighbor ◽  
Experimental Results ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Linear Discriminant ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine

Myoelectric pattern recognition (MPR) is used to detect user’s intention to achieve a smooth interaction between human and machine. The performance of MPR is influenced by the features extracted and the classifier employed. A kernel extreme learning machine especially radial basis function extreme learning machine (RBF-ELM) has emerged as one of the potential classifiers for MPR. However, RBF-ELM should be optimized to work efficiently. This paper proposed an optimization of RBF-ELM parameters using hybridization of particle swarm optimization (PSO) and a wavelet function. These proposed systems are employed to classify finger movements on the amputees and able-bodied subjects using electromyography signals. The experimental results show that the accuracy of the optimized RBF-ELM is 95.71% and 94.27% in the healthy subjects and the amputees, respectively. Meanwhile, the optimization using PSO only attained the average accuracy of 95.53 %, and 92.55 %, on the healthy subjects and the amputees, respectively. The experimental results also show that SW-RBF-ELM achieved the accuracy that is better than other well-known classifiers such as support vector machine (SVM), linear discriminant analysis (LDA) and k-nearest neighbor (kNN).

Sign in / Sign up

Export Citation Format

Share Document