Eigenface Approach Research Articles

Application Of the Eigenface Method in The Presence System Using Web-Based Face Recognition

The swift progress of technology has yielded numerous advantages, particularly for organizations engaged in vocational education, such as E-learning Marketplace in Bandung (Luarsekolah). One of the advantages of technology in vocational education is the use of attendance system technology. Luarsekolah's attendance system employs a conventional fingerprint recognition method, which frequently encounters difficulties when employees attempt to clock in, as their fingerprints are occasionally not acknowledged. Hence, creating a system that facilitates and streamlines employee attendance without requiring physical interaction is imperative. To address this, a facial recognition system utilizing the eigenface approach, with a development methodology based on the waterfall method, is suggested. Using the Eigenface PCA (Principal Component Analysis) technique, this system can function in real time. The research findings demonstrate the efficacy of the eigenface approach in real-time applications. The eigenface approach incorporates multiple validations to ensure the system's accuracy when employees utilize it. The approach incorporates several validations, such as presenting a message when an employee's face is near the camera and indicating a message when the employee's face is not directed toward the camera. If the camera misses adequate lighting, the system will automatically modify the lighting conditions to enable the camera to capture the employee's facial features.

Security Technologies Using Facial Recognition

Abstract
 
 Faces are one of the simplest methods to determine a person's identity. Face recognition is a unique identifying method that uses an individual's traits to determine the identity of that individual. The proposed recognition process is divided into two stages: face recognition and object recognition. Unless the item is very close, this procedure is very rapid for humans. The recognition of human faces is introduced next. The stage is then reproduced and used as a model for facial image recognition (face recognition). That's one of the professionally created and well-researched biometrics procedures. The eigenface approach and the Fisher face method are two common face recognition pattern algorithms that have been developed. Recognition of facial images The Eigenface approach is based on the reduction of face dimensional space for facial traits using Principal Component Analysis (PCA). The major goal of applying PCA on face recognition was to generate Eigen faces (face space) by identifying the eigenvector corresponding to the face image's biggest eigenvalue. Image processing and security systems are areas of interest in this research face recognition integrated into a security system.
 
 Keywords: face recognition, security systems, camera, python;

Eigenface approach based human face recognition

This paper describe the principle of eigenface approach to detection and identification human face. In this approach, the features extracted from face images are individual, hidden, light insensitive, and effective to recognition. Wherever approach treats the face recognition as two dimensions recognition problems, taking advantage of the fact that faces are normally upright and thus may be described by a small set of two characteristics view. The face images for training and testing are selected carefully from Olivetti and Oracle Research Laboratory (ORL) face database in order to have minmum pose variation. The eigenface approach is proposed seems to be an adequate method to be used in face recognition due to its simplicity, speed, and learning capability.

Face Detection and Recognition from Real Time Video or Recoded Video using Haar Features with Viola Jones Algorithm and Eigenface Approach with PCA.

Looking at somebody's face is one of the easiest methods to figure out who they are. Face detection, which occurs incredibly rapidly in humans except when the object is positioned at a short distance away, and introduction, which recognizes a face as an individual by using a person's characteristics to identify who they are, are the two main phases of the human face recognition procedure. In our proposed research, we have used Haar Features with Viola-Jones algorithm for face detection and Eigenface approach with Principal Component Analysis (PCA) for face recognition. The major objective of this proposed research method is to analyze faces to detect and also recognize the unknown faces from real-time webcam or recorded video. If our method successfully detects and recognizes unknown faces with competent accuracy, we will be able to prevent future crimes through precautionary measures.

Automated Attendance System Based on Facial Recognition using Viola-Jones Algorithm

Facial Recognition is the most used technology nowadays. Apart from Bio-metrics, Iris scan, fingerprint recognition methodologies, facial recognition is emerging recognition methodology these days. One of the most effective applications of this methodology is automated attendance using facial recognition, which is contact less, secure, and effective unlike in tradition way (manual attendance) it saves more time. Methodology used in this project involves Viola-Jones algorithm for face detection and Eigenfaces approach for feature selection and classification. In Viola-jones algorithm inputs are taken as captured images of individual persons and produce a dataset containing cropped images of individual and these dataset is directed to Eigenfaces approach as input and training of data occurs through the process of calculating eigen vectors for each eigenface. At the time of testing, Euclidean distance between eigen vectors of testing image and eigen vectors of trained eigen faces determines the matched individual. Facial recognition can also be done with PCA, which has 79.6 percent accuracy, and LBPH, which has 90.23 percent accuracy. However, when employing the Eigenfaces technique, the accuracy is 93.07 percent. MATLAB software with Computer Vision Toolbox and Deep Learning Toolbox is used for this work.

SIMULASI PENERAPAN TEKNOLOGI FACE RECOGNITION PADA APLIKASI PENDATAAN KEHADIRAN KARYAWAN / KARYAWATI TERINTEGRASI E-MON SISTEM PENGGAJIAN DENGAN PENDEKATAN EIGENFACE DAN UXD LARAVEL VERSI 5.6 BERBASIS CLOUD SERVICE

Face recognition is a face-oriented recognition method by comparing patterns that have previously been stored in the database. In general, facial image recognition systems are divided into two types, namely feature based systems and image based systems. To carry out facial image recognition, there are several kinds of approach algorithms that can support the processing of the face image. One approach algorithm that is well known and capable of processing images with a good level of accuracy is the eigenface approach. By using the eigenface approach, the face image will be processed by performing a matrix calculation, which results in an eucludian distance to be able to get the suitability of the face image. so this research produces a result which includes: the results of the test on the FGD Functional says for the accepted percentage is: 73% for not accepted is 27%, for Non-Functional FGD says for the accepted percentage is 82% and not accepted is 18 %, and for the results of testing the adaptation of ISO 9126 in the good category with a percentage result of 79%, and the last test result, namely the Blackbox Testing test to be accepted is 83% and for the results the percentage that is not accepted is 17%

SIMULASI PENERAPAN TEKNOLOGI FACE RECOGNITION PADA APLIKASI PENDATAAN KEHADIRAN KARYAWAN / KARYAWATI TERINTEGRASI E-MON SISTEM PENGGAJIAN DENGAN PENDEKATAN EIGENFACE DAN UXD LARAVEL VERSI 5.6 BERBASIS CLOUD SERVICE

Abstract — Face recognition is a face-oriented recognition method by comparing patterns that have previously been stored in the database. In general, facial image recognition systems are divided into two types, namely feature based systems and image based systems. To carry out facial image recognition, there are several kinds of approach algorithms that can support the processing of the face image. One approach algorithm that is well known and capable of processing images with a good level of accuracy is the eigenface approach. By using the eigenface approach, the face image will be processed by performing a matrix calculation, which results in an eucludian distance to be able to get the suitability of the face image. so this research produces a result which includes: the results of the test on the FGD Functional says for the accepted percentage is: 73% for not accepted is 27%, for Non-Functional FGD says for the accepted percentage is 82% and not accepted is 18 %, and for the results of testing the adaptation of ISO 9126 in the good category with a percentage result of 79%, and the last test result, namely the Blackbox Testing test to be accepted is 83% and for the results the percentage that is not accepted is 17%

Secure facial recognition in the encrypted domain using a local ternary pattern approach

Automatic facial recognition is fast becoming a reliable method for identifying individuals. Due to its reliability and unobtrusive nature facial recognition has been widely deployed in law enforcement and civilian application. Recent implementations of facial recognition systems on public cloud computing infrastructures have raised strong concerns regarding an individual's privacy. In this paper, we propose and implement a novel approach for facial recognition in the encrypted domain. This allows for facial recognition to be performed without revealing the actual image unnecessarily as the features stay encrypted at all times. Our proposed system exploits the homomorphic properties of the Paillier cryptosystem and performs Euclidean distance calculations using encrypted data. We propose to represent the images using a radial Local Ternary Pattern approach where a higher than proposed radius is used to extract the image features. Our proposed system has been evaluated using two publicly available datasets and has also been compared against the previously used eigenface approach in the encrypted domain and the obtained results justify the feasibility of the proposed system.

Human Face Recognition from Part of a Facial Image based on Image Stitching

Most of the current techniques for face recognition require the presence of a full face of the person to be recognized, and this situation is difficult to achieve in practice, the required person may appear with a part of his face, which requires prediction of the part that did not appear. Most of the current forecasting processes are done by what is known as image interpolation, which does not give reliable results, especially if the missing part is large. In this work, we adopted the process of stitching the face by completing the missing part with the flipping of the part shown in the picture, depending on the fact that the human face is characterized by symmetry in most cases. To create a complete model, two facial recognition methods were used to prove the efficiency of the algorithm. The selected face recognition algorithms that are applied here are Eigenfaces and geometrical methods. Image stitching is the process during which distinctive photographic images are combined to make a complete scene or a high-resolution image. Several images are integrated to form a wide-angle panoramic image. The quality of the image stitching is determined by calculating the similarity among the stitched image and original images and by the presence of the seam lines through the stitched images. The Eigenfaces approach utilizes PCA calculation to reduce the feature vector dimensions. It provides an effective approach for discovering the lower-dimensional space. In addition, to enable the proposed algorithm to recognize the face, it also ensures a fast and effective way of classifying faces. The phase of feature extraction is followed by the classifier phase. Displacement classifiers using square Euclidean and City-Block distances are used. The test results demonstrate that the proposed algorithm gave a recognition rate of around 95%, to validate the proposed algorithm; it compared to the existing CNN and Multibatch estimator method.

Classifier for Face Recognition Based on Deep Convolutional - Optimized Kernel Extreme Learning Machine

Face recognition task has been an active research area in recent years in computer vision and biometrics. Feature extraction and classification are the most significant steps for accurate face recognition systems. Conventionally, the Eigenface approach or frequency domain features have been used for feature extraction, but they are not invariant to outdoor conditions like lighting, pose, expression, and occlusion. Multiple convolutional and pooling layers of Deep Learning Networks (DLN) will efficiently extract the face database’s high-level features in the present work. These features have given to the Kernel Extreme Learning Machine (KELM) classifier, whose parameters have optimized using Particle Swarm Optimization (PSO). The proposed Deep Convolutional-Optimized Kernel Extreme Learning Machine (DC-OKELM) algorithm leads to better performance results and fast learning speed than classification using deep neural networks. The performance of DC-OKELM has evaluated on four standards face databases: AT&T, CMU-PIE, Yale Faces, and UMIST. Experimental results have compared with other state-of-the-art classifiers in terms of error rate and network training time, which shows the proposed DC-OKELM classifier’s effectiveness.

Mobile Face Recognition Application using Eigen face Approaches for Android

Face recognition is one of current biometrics identification methods, that based on the measuring to one of human biological characteristics and utilize them to recognize individuals. these characteristics which are called biometric they are hard to fake because they identify a person by measuring one of its biological characteristics such as (finger print, iris print and face print). With the rapid improvement of mobile technologies that happen in last decade face recognition process can make using mobile phone, this paper explains the building of mobile face recognition system using Eigen face approach, Experimental results have been tested on a local data-set that has been created to analyze the efficiency of the application in various cases including different illumination conditions, variation of view, and orientation, the recognition rate of the application when testing on Galaxy Grand Prime + was 78.4. while The recognition rate when testing on Galaxy Note 5 was 82.4. The accuracy of this application can reach to 100% if we use camera with high accuracy and on good light condition.

Eigenfaces Technique, an Improved Face Recognition Approach Using Neural Network

Image analysis and understanding, stands tall amongst all the technologies and face recognition is an eminent part of it. A face database is maintained as a logbook to identify an input face. This is accomplished by mere comparison amongst the face database. There are several face recognition techniques, of which, symmetry, Elastic Bunch Graph Matching (EBGM), and analytic-to-holistic recognition have been explored in this research paper. Other peculiar approaches like image based face recognition techniques like MLP, convolutional neural network, eigenfaces, associative neural networks, recirculation neural network and independent component analysis have been thoroughly discussed. Two vibrant face recognition databases, UMIST and ORL have proved to be extremely important in analyzing the results of face recognition. Eigen Face value approach has been anticipated with the associated analysis of results of face recognition. Another approach in face recognition is optimized multiperceptron, which will be acting as the reference to the optimized eigenfaces approach in this research paper, hence making this study more efficient through comparison.

Analysis and Recognition of Facial Image by Eigenface Approach

In this paper, algorithm is developed based on eigenface algorithm to recognize facial image. After implementing the algorithm a reference image identified from a predefined facial image dataset. In preprocessing stage the images are resized to a consistent size. The performance of algorithm is analyzed by using pose and size of training dataset. It is seen from the result that robustness of the eigenface algorithm is good.

Three-Dimensional Eigenbrain for the Detection of Subjects and Brain Regions Related with Alzheimer's Disease.

Considering that Alzheimer's disease (AD) is untreatable, early diagnosis of AD from the healthy elderly controls (HC) is pivotal. However, computer-aided diagnosis (CAD) systems were not widely used due to its poor performance. Inspired from the eigenface approach for face recognition problems, we proposed an eigenbrain to detect AD brains. Eigenface is only for 2D image processing and is not suitable for volumetric image processing since faces are usually obtained as 2D images. We extended the eigenbrain to 3D. This 3D eigenbrain (3D-EB) inherits the fundamental strategies in either eigenface or 2D eigenbrain (2D-EB). All the 3D brains were transferred to a feature space, which encoded the variation among known 3D brain images. The feature space was named as the 3D-EB, and defined as eigenvectors on the set of 3D brains. We compared four different classifiers: feed-forward neural network, support vector machine (SVM) with linear kernel, polynomial (Pol) kernel, and radial basis function kernel. The 50x10-fold stratified cross validation experiments showed that the proposed 3D-EB is better than the 2D-EB. SVM with Pol kernel performed the best among all classifiers. Our "3D-EB + Pol-SVM" achieved an accuracy of 92.81% ± 1.99% , a sensitivity of 92.07% ± 2.48% , a specificity of 93.02% ± 2.22% , and a precision of 79.03% ± 2.37% . Based on the most important 3D-EB U1, we detected 34 brain regions related with AD. The results corresponded to recent literature. We validated the effectiveness of the proposed 3D-EB by detecting subjects and brain regions related to AD.

A Human Facial Expression Recognition Model Based on Eigen Face Approach

Emotion recognition through facial expression detection is one of the important fields of study for human-computer interaction. To detect a facial Expression one system need to come across various variability of human faces such as colour, posture, expression, orientation, etc. To detect the expression of a human face first it is required to detect the different facial features such as the movements of eye, nose, lips, etc. and then classify them comparing with trained data using a suitable classifier for expression recognition. In this research, a human facial expression recognition system is modelled using eigenface approach. The proposed method uses the HSV (Hue-Saturation-Value) colour model to detect the face in an image. PCA has been used for reducing the high dimensionality of the eigenspace and then by projecting the test image upon the eigenspace and calculating the Euclidean distance between the test image and mean of the eigenfaces of the training dataset the expressions are classified. A generic dataset is used for training purpose. The gray scale images of the face is used by the system to classify five basic emotions such as surprise, sorrow, fear, anger and happiness.

FACE RECOGNITION SYSTEM FOR MACHINE READABLE TRAVEL DOCUMENTS

This paper presents an upright frontal face recognition system, aimed to recognize faces on machine readable travel documents (MRTD). The system is able to handle large image databases with high processing speed and low detection and identification errors. In order to achieve high accuracy eyes are detected in the most probable regions, which narrows search area and therefore reduces computation time. Recognition is performed with the use of eigenface approach. The paper introduces eigenface basis ranking measure, which is helpful in challenging task of creating the basis for recognition purposes. To speed up identification process we split the database into males and females using high - performance AdaBoost classifier. At the end of the paper the results of the tests in speed and accuracy are given.

Study of Different Face Recognition Algorithms and Challenges

At present face recognition has wide area of applications such as security, law enforcement. Imaging conditions, Orientation, Pose and presence of occlusion are huge problems associated with face recognition. The performance of face recognition systems decreases due to these problems. Discriminant Analysis (LDA) or Principal Components Analysis (PCA) is used to get better recognition results. Human face contains relevant information that can extracted from face model developed by PCA technique. Principal Components Analysis method uses eigenface approach to describe face image variation. A face recognition technique that is robust to all situations is not available. Some techniques are better in case of illumination, some for pose problem and some for occlusion problem. This paper presents some algorithms for face recognition.

LDA-based Non-negative Matrix Factorization for Supervised Face Recognition

In PCA based face recognition, the basis imagesmay contain negative pixels and thus do not facilitatephysical interpretation. Recently, the technique of nonnegativematrix Factorization (NMF) has been applied to face recognition: the non-negativity constraint of NMF yieldsa localized parts-based representation which achieves arecognition rate that is on par with the eigenface approach. In this paper, we propose a new variation of the NMF algorithm that incorporates training information in a supervised learning setting. We integrate an additional term basedon Fisher’s Linear Discriminant Analysis into the NMF algorithm and prove that our new update rule can maintainthe non-negativity constraint under a mild condition andhence preserve the intuitive meaning for the base vectors and weight vectors while facilitating the supervised learning of within-class and between-class information. We tested our new algorithm on the well-known ORL database, CMU PIE database and FERET database, and the results from experiments are very encouraging compared with traditional techniques including the original NMF, the Eigenface method, the sequential NMF+LDA method and the Fisherface method.

Age Dependent Face Recognition using Eigenface

Face recognition is the most successful form of human surveillance. Face recognition technology, is being used to improve human efficiency when recognition faces, is one of the fastest growing fields in the biometric industry. In the first stage, the age is classified into eleven categories which distinguish the person oldness in terms of age. In the second stage of the process is face recognition based on the predicted age. Age prediction has considerable potential applications in human computer interaction and multimedia communication. In this paper proposes an Eigen based age estimation algorithm for estimate an image from the database. Eigenface has proven to be a useful and robust cue for age prediction, age simulation, face recognition, localization and tracking. The scheme is based on an information theory approach that decomposes face images into a small set of characteristic feature images called eigenfaces, which may be thought of as the principal components of the initial training set of face images. The eigenface approach used in this scheme has advantages over other face recognition methods in its speed, simplicity, learning capability and robustness to small changes in the face image.

Over twenty years of eigenfaces

The inaugural ACM Multimedia Conference coincided with a surge of interest in computer vision technologies for detecting and recognizing people and their activities in images and video. Face recognition was the first of these topics to broadly engage the vision and multimedia research communities. The Eigenfaces approach was, deservedly or not, the method that captured much of the initial attention, and it continues to be taught and used as a benchmark over 20 years later. This article is a brief personal view of the genesis of Eigenfaces for face recognition and its relevance to the multimedia community.