scholarly journals Efficient K-Nearest Neighbor Searches for Multiple-Face Recognition in the Classroom based on Three Levels DWT-PCA

2017 ◽  
Vol 8 (11) ◽  
Author(s):  
Hadi Santoso ◽  
Agus Harjoko ◽  
Agfianto Eko
Keyword(s):  
Face Recognition ◽  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Searches

scholarly journals Optimization of deep learning features for age-invariant face recognition

2020 ◽  
Vol 10 (2) ◽  
pp. 1833
Author(s):  
Amal A. Moustafa ◽  
Ahmed Elnakib ◽  
Nihal F. F. Areed
Keyword(s):  
Deep Learning ◽  
Face Recognition ◽  
Nearest Neighbor ◽  
Recognition Rate ◽  
Manhattan Distance ◽  
Distance Metrics ◽  
K Nearest Neighbor ◽  
Face Images ◽  
Knn Classifier ◽  
Deep Learning Features

This paper presents a methodology for Age-Invariant Face Recognition (AIFR), based on the optimization of deep learning features. The proposed method extracts deep learning features using transfer deep learning, extracted from the unprocessed face images. To optimize the extracted features, a Genetic Algorithm (GA) procedure is designed in order to select the most relevant features to the problem of identifying a person based on his/her facial images over different ages. For classification, K-Nearest Neighbor (KNN) classifiers with different distance metrics are investigated, i.e., Correlation, Euclidian, Cosine, and Manhattan distance metrics. Experimental results using a Manhattan distance KNN classifier achieves the best Rank-1 recognition rate of 86.2% and 96% on the standard FGNET and MORPH datasets, respectively. Compared to the state-of-the-art methods, our proposed method needs no preprocessing stages. In addition, the experiments show its privilege over other related methods.

scholarly journals Pengenalan Wajah Menggunakan Metode Linear Discriminant Analysis dan k Nearest Neighbor

2017 ◽  
Vol 9 (1) ◽  
pp. 1-9
Author(s):  
Fandiansyah Fandiansyah ◽  
Jayanti Yusmah Sari ◽  
Ika Putri Ningrum
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Nearest Neighbor ◽  
Experimental Result ◽  
K Nearest Neighbor ◽  
Linear Discriminant ◽  
Nearest Neighbor Classifier ◽  
Neighbor Classifier

Face recognition is one of the biometric system that mostly used for individual recognition in the absent machine or access control. This is because the face is the most visible part of human anatomy and serves as the first distinguishing factor of a human being. Feature extraction and classification are the key to face recognition, as they are to any pattern classification task. In this paper, we describe a face recognition method based on Linear Discriminant Analysis (LDA) and k-Nearest Neighbor classifier. LDA used for feature extraction, which directly extracts the proper features from image matrices with the objective of maximizing between-class variations and minimizing within-class variations. The features of a testing image will be compared to the features of database image using K-Nearest Neighbor classifier. The experiments in this paper are performed by using using 66 face images of 22 different people. The experimental result shows that the recognition accuracy is up to 98.33%. Index Terms—face recognition, k nearest neighbor, linear discriminant analysis.

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.

MMAS Algorithm for Features Selection Using 1D-DWT for Video-Based Face Recognition in the Online Video Contextual Advertisement User-Oriented System

2017 ◽  
Vol 25 (4) ◽  
pp. 103-124 ◽  
Author(s):  
Le Nguyen Bao ◽  
Dac-Nhuong Le ◽  
Gia Nhu Nguyen ◽  
Le Van Chung ◽  
Nilanjan Dey
Keyword(s):  
Feature Selection ◽  
Face Recognition ◽  
Nearest Neighbor ◽  
Discrete Wavelet ◽  
Feature Subset ◽  
Features Selection ◽  
Ant System ◽  
K Nearest Neighbor ◽  
Video Based Face Recognition ◽  
Optimal Feature

Face recognition is an importance step which can affect the performance of the system. In this paper, the authors propose a novel Max-Min Ant System algorithm to optimal feature selection based on Discrete Wavelet Transform feature for Video-based face recognition. The length of the culled feature vector is adopted as heuristic information for ant's pheromone in their algorithm. They selected the optimal feature subset in terms of shortest feature length and the best performance of classifier used k-nearest neighbor classifier. The experiments were analyzed on face recognition show that the authors' algorithm can be easily implemented and without any priori information of features. The evaluated performance of their algorithm is better than previous approaches for feature selection.

Web data retrieval: solving spatial range queries using k-nearest neighbor searches

2008 ◽  
Vol 13 (4) ◽  
pp. 483-514 ◽  
Author(s):  
Wan D. Bae ◽  
Shayma Alkobaisi ◽  
Seon Ho Kim ◽  
Sada Narayanappa ◽  
Cyrus Shahabi
Keyword(s):  
Nearest Neighbor ◽  
Data Retrieval ◽  
Range Queries ◽  
K Nearest Neighbor ◽  
Web Data ◽  
Spatial Range ◽  
Nearest Neighbor Searches

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%.

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