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.

Face Recognition by PCA and Improved LBP Fusion Algorithm

2015 ◽  
Vol 734 ◽  
pp. 562-567 ◽  
Author(s):  
En Zeng Dong ◽  
Yan Hong Fu ◽  
Ji Gang Tong
Keyword(s):  
Face Recognition ◽  
Recognition Rate ◽  
Texture Feature ◽  
Principal Component ◽  
Texture Features ◽  
Rotation Invariant ◽  
Fusion Algorithm ◽  
Face Images ◽  
The Face ◽  
Pca Method

This paper proposed a theoretically efficient approach for face recognition based on principal component analysis (PCA) and rotation invariant uniform local binary pattern texture features in order to weaken the effects of varying illumination conditions and facial expressions. Firstly, the rotation invariant uniform LBP operator was adopted to extract the local texture feature of the face images. Then PCA method was used to reduce the dimensionality of the extracted feature and get the eigenfaces. Finally, the nearest distance classification was used to distinguish each face. The method has been accessed on Yale and ATR-Jaffe face databases. Results demonstrate that the proposed method is superior to standard PCA and its recognition rate is higher than the traditional PCA. And the proposed algorithm has strong robustness against the illumination changes, pose, rotation and expressions.

scholarly journals Half-face based recognition using principal component analysis

2021 ◽  
Vol 22 (3) ◽  
pp. 1404
Author(s):  
Ahmed M. Alkababji ◽  
Sara Raed Abd
Keyword(s):  
Principal Component Analysis ◽  
Face Recognition ◽  
Principal Component ◽  
Component Analysis ◽  
Recognition Method ◽  
Face Images ◽  
The Face ◽  
Considerable Problem ◽  
Full Face ◽  
Pca Algorithm

<span lang="EN-US">Face recognition is a considerable problem in the field of image processing. It is used daily in various applications from personal cameras to forensic investigations. Most of the provides solutions proposed based on full-face images, are slow to compute and need more storage. In this paper, we propose an effective way to reduce the features and size of the database in the face recognition method and thus we get an increase in the speed of discrimination by using half of the face. Taking advantage of face symmetry, the first step is to divide the face image into two halves, then the left half is processed using the principal component analysis (PCA) algorithm, and the results are compared by using Euclidian distance to distinguish the person. The system was trained and tested on ORL database. It was found that the accuracy of the system reached up to 96%, and the database was minimized by 46% and the running time was decreased from 120 msec to 70 msec with a 41.6% reduction.</span>

scholarly journals RECOGNIZING FACES USING INDIVIDUAL EIGEN SUBSPACES TECHNIQUE WITH FUZZY RULES METHODOLOGY

2015 ◽  
pp. 164-169
Author(s):  
AISHWARYA P ◽  
KARNAN MARCUS
Keyword(s):  
Face Recognition ◽  
Principal Component ◽  
Rejection Rate ◽  
Security Systems ◽  
Major Objective ◽  
Face Representation ◽  
Face Images ◽  
The Face ◽  
Small Set ◽  
Effective Analysis

This paper proposes a new methodology of recognizing face using Individual Eigen Subspaces and it’s implemented in the field of Image Processing for Personnel verification or recognition. A major objective of this work is to develop a tool for face recognition, which can help in quicker and effective analysis of a face from the face set, thus reducing false acceptance rate and false rejection rate. Face recognition has been widely explored in the past years. A lot of techniques have been applied in various applications. Robustness and reliability have become more and more important for these applications especially in security systems. In this thesis, a variety of approaches for face recognition are reviewed first. These approaches are classified according to three basic tasks: face representation, face detection, and face identification. An implementation of the appearance-based face recognition method, the eigenface recognition approach, is reported. This method utilizes the idea of the principal component analysis and decomposes face images into a small set of characteristic feature images called eigenfaces. This proposed work is intended to develop, multiple face Eigen subspaces. With each one is corresponding to one known subject privately, rather than all individuals sharing one universal subspace as in the traditional eigenface method. Compared with the traditional single subspace face representation, the proposed method captures the extra personal difference to the most possible extent, which is crucial to distinguish between individuals, and on the other hand, it throws away the most intrapersonal difference and noise in the input.

SUBSPACE-BASED FACE RECOGNITION: OUTLIER DETECTION AND A NEW DISTANCE CRITERION

2005 ◽  
Vol 19 (04) ◽  
pp. 479-493 ◽  
Author(s):  
PEI CHEN ◽  
DAVID SUTER
Keyword(s):  
Face Recognition ◽  
Outlier Detection ◽  
Principal Component ◽  
Dimensional Subspace ◽  
Recognition Algorithm ◽  
New Techniques ◽  
Face Images ◽  
The Face ◽  
Low Dimensional ◽  
New Strategies

Illumination effects, including shadows and varying lighting, make the problem of face recognition challenging. Experimental and theoretical results show that the face images under different illumination conditions approximately lie in a low-dimensional subspace, hence principal component analysis (PCA) or low-dimensional subspace techniques have been used. Following this spirit, we propose new techniques for the face recognition problem, including an outlier detection strategy (mainly for those points not following the Lambertian reflectance model), and a new error criterion for the recognition algorithm. Experiments using the Yale-B face database show the effectiveness of the new strategies.

scholarly journals A Novel Face Recognition Algorithm Using Gabor - based KPCA

2018 ◽  
Vol 7 (2) ◽  
pp. 124
Author(s):  
Umasankar Ch ◽  
D. Naresh Kumar ◽  
Md. Abdul Rawoof ◽  
D. Khalandar Basha ◽  
N. Madhu
Keyword(s):  
Face Recognition ◽  
Structural Characteristics ◽  
Feature Space ◽  
Facial Feature ◽  
Input Image ◽  
Recognition Algorithm ◽  
Nonlinear Mapping ◽  
Kernel Pca ◽  
Face Images ◽  
The Face

The Gabor wavelets are used to extract facial features, and then a doubly nonlinear mapping kernel PCA (DKPCA) is proposed to perform feature transformation and face recognition. The conventional kernel PCA nonlinearly maps an input image into a high-dimensional feature space in order to make the mapped features linearly separable. However, this method does not consider the structural characteristics of the face images, and it is difficult to determine which nonlinear mapping is more effective for face recognition. In this work, a new method of nonlinear mapping, which is performed in the original feature space, is defined. The proposed nonlinear mapping not only considers the statistical properties of the input features, but also adopts an Eigen mask to emphasize those important facial feature points The proposed algorithm is evaluated based on the Yale database, the AR database, the ORL database and the YaleB database.

New Pattern Recognition Method Based on Wavelet De-Noising and Kernel Principal Component Analysis

2012 ◽  
Vol 235 ◽  
pp. 74-78 ◽  
Author(s):  
Jia Jun Zhang ◽  
Li Juan Liang
Keyword(s):  
Principal Component Analysis ◽  
Image Recognition ◽  
Principal Component ◽  
Component Analysis ◽  
Face Image ◽  
Kernel Principal Component Analysis ◽  
Support Vector ◽  
Svm Classifier ◽  
Face Images ◽  
The Face

The background noise influences the face image recognition greatly. It is crucial to remove the noise signals prior to the face image recognition processing. For this purpose, the wavelet de-noising technology has combined with the kernel principal component analysis (KPCA) to identify face images in this paper. The wavelet de-noising technology was firstly used to remove the noise signals. Then the KPCA was employed to extract useful principal components for the face image recognition. By doing so, the dimensionality of the feature space can be reduced effectively and hence the performance of the face image recognition can be enhanced. Lastly, a support vector machine (SVM) classifier was used to recognize the face images. Experimental tests have been conducted to validate and evaluate the proposed method for the face image recognition. The analysis results have showed high performance of the newly proposed method for face image identification.

scholarly journals Face Recognition Based Attendance System

2019 ◽  
Vol 8 (12) ◽  
pp. 520-525
Keyword(s):  
Face Recognition ◽  
Human Error ◽  
Principal Component ◽  
Input Image ◽  
Human Errors ◽  
Effective Manner ◽  
The Face ◽  
Alternative Means ◽  
Number Of Faces ◽  
Set Up

The objective of the attendance system is to provide an alternative means to the traditional attendance system which consumes 10 to 15 minutes of time in 50 minutes of lecture hour. It also aims at eliminating human errors and proxy in recording the attendance of the student. This can be achieved by using face recognition for monitoring the attendance of the students in a class. The face recognition process is carried out by using the Cognitive Face API which follows the Principal Component Analysis (PCA) algorithm. Initially, the dataset of the students in a class are collected. The dataset is collected in a manner that for each student, a set of 25 images in various angles is collected. The features are extracted from the images that are collected by using the cognitive face API and the database is formed. The image of the class in columns is acquired immediately, when the input image is acquired by using a mechanical set up which captures image based on hour, the number of faces in the input image is detected. The detected faces are cropped and then stored in a folder. The features of the cropped faces are also extracted and it is compared and matched with the features in the database. When the feature matches, the attendance is marked for the particular student in the spreadsheet and then the attendance report of the class is being uploaded in the web-page. Thus, the attendance of the student can be recorded in an effective manner. This paper also helps in avoiding human error which is unavoidable

Multi-View Face Recognition Based on BP Neural Network and KPCA

2014 ◽  
Vol 1030-1032 ◽  
pp. 1737-1740
Author(s):  
Xin Wang ◽  
He Pan
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Face Recognition ◽  
Bp Neural Network ◽  
Recognition Rate ◽  
Principal Component ◽  
Experimental Results ◽  
Kernel Principal Component Analysis ◽  
Face Images ◽  
Network Methods

This paper introduces the research background of computer face recognition technology, and puts forward a method of using kernel principal component analysis (KPCA) method and improved BP neural network methods for analysis and identification of multi view face images. The experimental results show that this algorithm is both effective and accurate. It achieved a higher recognition rate and excellent resistance to noise.

scholarly journals Recognition of Reconstructed Frontal Face Images Using FFT-PCA/SVD Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Louis Asiedu ◽  
Felix O. Mettle ◽  
Joseph A. Mensah
Keyword(s):  
Face Recognition ◽  
Principal Component ◽  
Recognition Algorithm ◽  
Environmental Constraints ◽  
Biometric Data ◽  
Face Images ◽  
The Face ◽  
Left And Right ◽  
Value Decomposition ◽  
User Friendly

Face recognition has gained prominence among the various biometric-based methods (such as fingerprint and iris) due to its noninvasive characteristics. Modern face recognition modules/algorithms have been successful in many application areas (access control, entertainment/leisure, security system based on biometric data, and user-friendly human-machine interfaces). In spite of these achievements, the performance of current face recognition algorithms/modules is still inhibited by varying environmental constraints such as occlusions, expressions, varying poses, illumination, and ageing. This study assessed the performance of Principal Component Analysis with singular value decomposition using Fast Fourier Transform (FFT-PCA/SVD) for preprocessing the face recognition algorithm on left and right reconstructed face images. The study found that average recognition rates for the FFT-PCA/SVD algorithm were 95% and 90% when the left and right reconstructed face images are used as test images, respectively. The result of the paired sample t-test revealed that the average recognition distances for the left and right reconstructed face images are not significantly different when FFT-PCA/SVD is used for recognition. FFT-PCA/SVD is recommended as a viable algorithm for recognition of left and right reconstructed face images.

Face recognition in an intelligent door lock with ResNet model based on deep learning

2021 ◽  
pp. 1-11
Author(s):  
Suphawimon Phawinee ◽  
Jing-Fang Cai ◽  
Zhe-Yu Guo ◽  
Hao-Ze Zheng ◽  
Guan-Chen Chen
Keyword(s):  
Face Recognition ◽  
Detection System ◽  
Low Cost ◽  
Ease Of Use ◽  
Raspberry Pi ◽  
Training Methods ◽  
Graphics Recognition ◽  
Face Images ◽  
The Face ◽  
Door Lock

Internet of Things is considerably increasing the levels of convenience at homes. The smart door lock is an entry product for smart homes. This work used Raspberry Pi, because of its low cost, as the main control board to apply face recognition technology to a door lock. The installation of the control sensing module with the GPIO expansion function of Raspberry Pi also improved the antitheft mechanism of the door lock. For ease of use, a mobile application (hereafter, app) was developed for users to upload their face images for processing. The app sends the images to Firebase and then the program downloads the images and captures the face as a training set. The face detection system was designed on the basis of machine learning and equipped with a Haar built-in OpenCV graphics recognition program. The system used four training methods: convolutional neural network, VGG-16, VGG-19, and ResNet50. After the training process, the program could recognize the user’s face to open the door lock. A prototype was constructed that could control the door lock and the antitheft system and stream real-time images from the camera to the app.

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