Eigenface Method Research Articles

From Face to Facies

Abstract This article considers Beckett’s faces in the light of Giorgio Agamben’s account of the face as a disputed biopolitical marker. Agamben refers to the face both in terms of social and juridical identity in ancient Rome and as an icon of contemporary biopolitics, as social identity gives way to biometric recognition. Beckett’s own face figures prominently in the series of machine-generated Eigenface portraits created by artist Trevor Paglen in 2017, and whose use of the Eigenface method invokes modern facial recognition technologies. The Eigenface is examined here in relation to a gallery of ghostly progenitors: the faces of Beckett’s late plays.

INTRUDER DETECTION SYSTEM USING MACHINE LEARNING

The face is one of the easiest ways to distinguish the individual identity of each other. Face recognition is a personal identification system that uses personal characteristics of a person to identify the person's identity. Human face recognition procedure basically consists of two phases, namely face detection, where this process takes place very rapidly in humans, except under conditions where the object is located at a short distance away, the next is the introduction, which recognize a face as individuals. Stage is then replicated and developed as a model for facial image recognition (face recognition) is one of the much-studied biometrics technology and developed by experts. There are two kinds of methods that are currently popular in developed face recognition pattern namely, Eigenface method and Fisher face method. Facial image recognition Eigenface method is based on the reduction of face dimensional space using Principal Component Analysis (PCA) for facial features. The main purpose of the use of PCA on face recognition using Eigen faces was formed (face space) by finding the eigenvector corresponding to the largest eigenvalue of the face image. The area of this project face detection system with face recognition is Image processing. The software requirements for this project is MATLAB software. Keywords: Digital Image Processing, Face Detection, Face Recognition, Motion Detection

IMPLEMENTATION OF AN IDENTIFICATION SYSTEM WITH FACIAL IMAGE PROCESSING (EIGENFACE) USING MATLAB APPLICATION

Facial recognition has emerged as a prominent personal identification system, especially in the industrial sector where traditional attendance card machines have been prevalent. However, manual systems pose several drawbacks, including susceptibility to fraud, lack of flexibility, and resource wastage in card production. To address these issues, this research proposes a shift from card-based attendance systems to a facial recognition-based system using MATLAB application. The study aims to design an identification system based on the eigenface method to process facial images. It begins with a thorough literature review to gather relevant references. Subsequently, a specialized MATLAB application was developed for face identification, and its accuracy was tested. The research utilizes trained data consisting of 45 photos and tested data consisting of 15 photos to evaluate the system's accuracy. The test results reveal a 100% accuracy rate in system identification. Notably, the accuracy varies when identifying faces with different background images, indicating the robustness of the MATLAB application. Overall, the findings suggest that MATLAB can effectively implement an image processing attendance system using the eigenface method.

Information-theoretical analysis of the neural code for decoupled face representation.

Processing faces accurately and efficiently is a key capability of humans and other animals that engage in sophisticated social tasks. Recent studies reported a decoupled coding for faces in the primate inferotemporal cortex, with two separate neural populations coding for the geometric position of (texture-free) facial landmarks and for the image texture at fixed landmark positions, respectively. Here, we formally assess the efficiency of this decoupled coding by appealing to the information-theoretic notion of description length, which quantifies the amount of information that is saved when encoding novel facial images, with a given precision. We show that despite decoupled coding describes the facial images in terms of two sets of principal components (of landmark shape and image texture), it is more efficient (i.e., yields more information compression) than the encoding in terms of the image principal components only, which corresponds to the widely used eigenface method. The advantage of decoupled coding over eigenface coding increases with image resolution and is especially prominent when coding variants of training set images that only differ in facial expressions. Moreover, we demonstrate that decoupled coding entails better performance in three different tasks: the representation of facial images, the (daydream) sampling of novel facial images, and the recognition of facial identities and gender. In summary, our study provides a first principle perspective on the efficiency and accuracy of the decoupled coding of facial stimuli reported in the primate inferotemporal cortex.

FaceBreach Alert: Multi-Modal Intrusion Detection emphasizing Face Recognition

Abstract: The face is one of the easiest ways to distinguish the individual identity of each other. Face recognition is a personal identification system that uses personal characteristics of a person to identify the person's identity. Human face recognition procedure basically consists of two phases, namely face detection, where this process takes place very rapidly in humans, except under conditions where the object is located at a short distance away, the next is the introduction, which recognize a face as individuals. Stage is then replicated and developed as a model for facial image recognition (face recognition) is one of the muchstudied biometrics technology and developed by experts. There are two kinds of methods that are currently popular in developed face recognition pattern namely, Eigenface method and Fisher face method. Facial image recognition Eigenface method is based on the reduction of face dimensional space using Principal Component Analysis (PCA) for facial features. The main purpose of the use of PCA on face recognition using Eigen faces was formed (face space) by finding the eigenvector corresponding to the largest eigenvalue of the face image. The area of this project face detection system with face recognition is Image processing. The software requirements for this project is MATLAB software.

Real-Time Detection of Face Mask Using Convolutional Neural Network

Masks are a simple barrier that can help us prevent transmission and spread of disease from other people who enter the body, avoid exposure to air pollution, and protect the face from the adverse effects of sunlight. However, many people are still ignorant about the importance of wearing masks for health. This study aims to detect whether or not to use masks in real-time by proposing a deep learning model to reduce illness and death caused by air pollution. The convolutional Neural Network (CNN) method was used in this research to detect facial recognition using a mask and not using a mask. The public dataset used in this research consists of 1300 images with 650 data using masks and 650 data without masks. The results of this study show that the proposed CNN method works well in detecting masked and non-masked faces in real time. The proposed method obtains an accuracy value of 97.5% at epoch 50. Previous research on mask detection using the Eigenface method yielded an accuracy of 88.89%, and another study using the Viola-Jones method yielded an accuracy of 95.5%. It can be concluded that this research can increase the accuracy value of previous studies. So, this research is feasible to be applied to the detection of mask use in real time.

Evaluation Method of Real-Time Face Detection

Object detection is one of computer technology using image or video. Face detection is closely related Image processing and computer vision are used to detect several objects including human faces, landscapes, cars, etc. Face detection algorithm aims to confirm if an image has a face as the object in it. In this study, face detection uses several methods, namely the Eigenface method, the Fisherface method, and the Local Binary Pattern Histogram (LBPH) method. This study used 10 different subjects. The test was carried out 15 times using each face detection method with constant distance. The face detection process in this study was simulated using JupyterLab. The result showed that LBPH method obtained the highest level of accuracy in between comparison among Fisherface method and the Eigenface method. The accuracy of the LBPH method is 93.90%, while the Eigenface method is 85% and the Fisherface method is 53.33%. Differences in face detection accuracy were found due to the low level of lighting in the room and the use of accessories on the subject.

Classroom Attendance using Face Detection and Facial Organs

The face is one of the easiest ways to distinguish the individual identity of each other. Face recognition is a personal identification system that uses personal characteristics of a person to identify the person's identity. Human face recognition procedure basically consists of two phases, namely face detection, where this process takes place very rapidly in humans, except under conditions where the object is located at a short distance away, the next is the introduction, which recognize a face as individuals. Stage is then replicated and developed as a model for facial image recognition (face recognition) is one of the much-studied biometrics technology and developed by experts. There are two kinds of methods that are currently popular in developed face recognition pattern namely, Eigenface method and Fisherface method. Facial image recognition Eigenface method is based on the reduction of face-dimensional space using Principal Component Analysis (PCA) for facial features. The main purpose of the use of PCA on face recognition using Eigen faces was formed (face space) by finding the eigenvector corresponding to the largest Eigenvalue of the face image. The area of this project face detection system with face recognition is Image processing. The software requirements for this project is matlab software.

基于主成分分析的猪脸特征提取研究

Individual identification and behavioural analysis of pigs is a key link in the intelligent management of a piggery, for which the computer vision technology based on application and improvement of deep learning model has become the mainstream. However, the operation of the model has high requirements to hardwares, also the model is of weak interpretability, which make it difficult to adapt to both the mobile terminals and the embedded applications. In this study, it is first put forward that the key facial features of pigs can be extracted by PCA method first before the eigen face method is adopted for verification tests to reach an average accuracy rate of 74.4%; the key features, for which the most identifiable ones are in turn, respectively, face contour, nose, ears and other parts of pigs, can be visualized, and this is different from the identification features adopted in manual identification. This method not only reduces the computational complexity but also is of strong interpretability, so it is suitable for both the mobile terminals and the embedded applications. In some way, this study provides a systematic and stable guidance for livestock and poultry production.

Prototype Face Attendance System Berbasis Raspberry PI Menggunakan Metode Eigenface di Program Studi Pendidikan Vokasional Teknik Elektro Untirta

Face recognition is one of the pattern recognition approaches for the purposes of identifying a person's face with a biometric approach. From this biometric pattern, it can be used as a means to identify the presence of a student. Assisted by the Raspberry Pi, this facial recognition can be done and it can be concluded whether the biometric pattern matches or not. This suitability will be a reference to indicate the presence of a student. So a Raspberry Pi-based Prototype Face Attendance System was made using the Eigenface method. Eigenface is an algorithm that works based on Principle Component Analysis (PCA). Tests were carried out by testing 30 students with a total of 30 photo samples per individual resulting in an accuracy value of 78.85% and error value is 21.15%.

Sistem Pencacah Sampah Berbasis Computer Vision Menggunakan Metode Eigenface

Indonesia is one of the largest countries in Asia, with a very dense population. According to data from The World Bank, the human population indicator in Indonesia in 2019 increased by 270,625.57 people. It shows that population density in Indonesia is related to world problems related to household waste. The household sector contributes as the top waste producer in Indonesia. Adipura data in 2019 stated that at least household waste contributed 36%. This figure is higher than the heap of waste generated from the traditional market, which reaches 24%. The accumulation of waste that occurs without any sorting of waste makes the waste more difficult to decompose and difficult to recycle. Apart from that, the current landfill causes air pollution. Therefore, it is necessary to increase public awareness regarding waste sorting and processing to overcome these problems. We propose to make a device that can help sort organic and inorganic waste with Artificial Intelligence technology based on Computer Vision using the Eigenface method and the Internet of Things. Eigenface is a method that has the working principle of using an XML file to perform face recognition. 1. Implementation of a Computer Vision-Based Garbage Counting System Using the Eigenface Method can run well, where when the system detects an organic object, the door of the counting machine can be opened, and vice versa if it detects inorganic, the machine door is closed. Accuracy results for organic are 70% and for inorganic 75%. It is due to the lack of variation in the dataset and changes in the object's physical condition.

Perancangan Aplikasi Absensi Karyawan Dengan Deteksi Wajah Menggunakan Metode Eigenface

Attendance is one of the most important repetitive transactions, because it is related to the productivity of employees and is one of the indicators of controlling human resources (HR) which aims to increase the potential of human resources and is used for efficiency. Current technological developments make it possible to create a system that can assist humans in recognizing a digital image. One area that is currently being developed is pattern recognition. This technology identifies a person's special physical characteristics. Examples of pattern recognition, for example, are face recognition, iris recognition, and fingerprint recognition. specific databases. Broadly speaking, the process of facial recognition is that the webcam camera captures the face. Then obtained a value of R, G, B. By using the initial processing, after that the facial processing stage is carried out using the eigenface method. In this eigenface method there are several core stages, namely: converting faces into matrices, calculating the FlatVector average, determining the eigenface values ??and carrying out the face identification process by looking for the eigenface values ??that are close to it. One of these facial recognition can be developed to become an attendance application that can be applied in the company to prevent manipulation of absenteeism by employees.

COMPARISON OF EIGENFACE AND FISHERFACE METHODS FOR FACE RECOGNITION

Abstract— Biometric information systems have been widely used in the fields of government, shopping centers, education and even security, which offer biological authentication so that the system can recognize its users more quickly. The parts of the human body are identified by a biometric system that has unique and specific characteristics, one of which is the face. Adjustment of facial image deals with objects that are never the same, due to the parts that can change. These changes are caused by facial expressions, light intensity, shooting angle, or changes in facial accessories. With this, the same object with several differences must be recognized as the same object. In this study, the data used were 388 face images and the sata test consisted of 30 face images. Before the face is tested, preprocessing and feature extraction are carried out using the Haar Cascade Classifier and then detected using Eigenface and Fisherface. Based on the research results, the Fisherface method is an algorithm that is accurate and efficient compared to the Eigenface algorithm. The Fisherface algorithm has an accuracy of 88%. while the Eigenface method has an accuracy rate of 76%.
 Keywords – Haar Cascade Classifier, Eigenface, Fisherface,.

Blur Face Recognition using Blur Metric and Some Variants of Supervised Distance Preserving Projection

This paper is focused on face recognition techniques in uncontrolled scenarios, specifically on the recognition of face images with blur effects. At first, the blur level of the testing image is determined using recently proposed blur metric. This blur metric value is used to blur the training set of gallery images using Gaussian filter. The blur level of training images is the same as that of the testing image. Two variants of Supervised Distance Preserving Projection (SDPP), SDPP as Semidefinite Least Square (SLS-SDPP) and Regularized Supervised Distance Preserving Projection (RSDPP), are used for extracting effective features of training and testing images. K-Nearest Neighbor classifier is used for matching. Numerical experiments were carried out on two benchmarking face data ORL and Yale. The performances of SLS-SDPP and RSDPP are compared with one of the leading methods Eigenface method. Experimental results show that the combination of blur metric and the feature extraction methods achieved outstanding performance in recognizing blur images of different levels and also outperforms the base methods and Eigenface method. Dhaka Univ. J. Sci. 69(3): 154-160, 2022 (June)

Implementation of eigenface method and support vector machine for face recognition absence information system

The student <span>attendance system is what is needed in the process of recording attendance in learning and the development of student achievement. Currently several modern educational institutions have implemented a student attendance system using QR codes or fingerprints, but many still use the traditional system by calculating the number of students attending class. Based on these problems, the solution that can be given is to implement a student attendance system through face matching in the Android mobile application with Eigenface algorithm and support vector machine (SVM) algorithm. Eigenface using the principal component analysis (PCA) method can be used to reduce the dimensions of facial images so that they produce fewer variables and are easier to handle. The results obtained are then entered into a pattern classifier to determine the identity of the owner of the face. This study used 100 facial data as test data and training data. The system test results show that the use of Eigenface with SVM as a classifier can provide a fairly high level of accuracy. For facial images that were included in the training, 91% of the identification was correct.</span>

Face and Liveliness Detection Based Smart Bank Locker

Face is one of the easiest ways to differentiate each other's identities. Facial recognition is a personal identification system that uses the personal characteristics of someone to identify who you are. The process of recognizing a person's face is basically it consists of two stages, namely facial recognition, in which this process occurs most frequently immediately to humans, except under circumstances where the object is available in a short period of time far, next is an introduction, which recognizes faces as individuals. Stage then it is repeated and developed as a model for face image recognition (face recognition) is one of the most widely studied biometric technologies and developed by experts. There are two types of currently popular methods of advanced facial recognition pattern namely, Eigen face method and Fisher face method. We use fisher face a monitoring method to verify any system. The location of this project is facing image processing system. The software requirements for this project are an anaconda.

Face recognition methods analysis

Face Recognition is one of the most important issues in Image processing tasks. It is important because it uses for various purposes in real world such as Criminal detection or for detecting fraud in passport and visa check in airports. Face book is a nice example of Face recognition application, when it sends notification to one user’s friends who are recognized by their images that user uploaded in face book page. To solve Face Recognition problem different methods are introduced such as Principal Component Analysis (PCA), Independent Component Analysis (ICA), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), and Hidden Markov Models (HMM) which are explained and analyzed. Also algorithms like; Eigen face, Fisher face and Local Binary Pattern Histogram (LBPH) which are simplest and most accurate methods are implemented in this project for AT&T dataset to recognize the most similar face to other faces in this data set. To this end these algorithms are explained and advantages and disadvantages of each one are analyzed as well. Consequently, the best method is selected with comparison between the results of face reconstruction by Engine face, Fisher face and Local binary pattern histogram methods. In this project Eigen face method has best result. It should be noted that for implementing face recognition algorithms color map methods are used to distinguish the facial features more precisely. In this work Rainbow color map in Eigen Face algorithm and HSV color map in Fisher Face algorithm are utilized and results shows that HSV color map is more accurate than rainbow color map.

Proposed approach to improve facial recognition techniques for occluded faces by Covid-19 mask protection

Wearing protective masks has become commonplace in several countries as a result of the epidemic connected to the Covid-19 virus. Wearing a mask obscures a considerable part of the face, making some facial recognition techniques difficult to complete and obstructing the operation of various identifying systems, such as access control systems. In this paper, we offer an original approach that allows many face recognition systems to continue to identify persons even when wearing protective masks. The proposed approach is mainly based on the prior use of skin detection techniques. We validated our method using the Eigenfaces method by the FEI database, which we supplemented with faces wearing protective masks. The evaluation results of our approach are very satisfactory.

Lecturer Attendance System using Face Recognition Application an Android-Based

In the field of industries, businesses, and offices the use of security systems and administrative management through data input using a face recognition system is being developed. Following the era of technological advances, communication and information systems are widely used in various administrative operational activities and company security systems because it is assessed by using a system that is based on facial recognition security levels and more secure data accuracy, the use of such systems is considered to have its characteristics so it is very difficult for other parties to be able to engineer and manipulate data produced as a tool to support the company's decision. Related to this, causing the author is to try to research the detection of facial recognition that is present in the application system through an Android device, then face recognition detection will be connected. and saved to the database that will be used as data about the presence of teaching lecturers. Using the local binary pattern histogram algorithm method to measure the face recognition system that can be applied as a technique in the attendance system of lecturers to be more effective and efficient. Based on testing by analyzing the false rate error rate and the false refusal rate can be seen that the average level of local binary pattern histogram accuracy reaches 95.71% better than through the Eigenface method which is equal to 76.28%.

PERBANDINGAN METODE PENGENALAN WAJAH MELALUI SURVEILLANCE BERBASIS PENGENALAN WAJAH

Until now, the facial recognition method is still very difficult to do, especially when the facial confirmation process is accurate in real time. Facial recognition methods that have been tested, such as eigenface, Local Binary Pattern Histogram (LBPH), and fisherface, are feasible methods to be tested directly by applying these three methods to facial recognition-based surveillance systems. This study aims to compare the level of real-time accuracy in personal identification on the three methods through 4 parameters, namely accuracy, FAR (False Acceptance Rate), FRR (False Rejection Rate), and time condition, namely lighting conditions based on time, namely morning, noon, afternoon and evening. Based on the results of the tests that have been done, the average accuracy is obtained, namely the highest average with the eigenface method with an accuracy of 73.64%, FAR 0.11%, FRR 0.15%, the LBPH method obtains the highest average with an accuracy of 80, 91%, FAR 0.13%, FRR 0.07%, and finally fisherface got the highest average with 90.00% accuracy, 0.05% FAR, 0.05% FRR, in identifying personal. The results obtained by the Fisherface method tend to have the highest accuracy value based on the average both in terms of accuracy and the lighting conditions that have been tested.