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Showing papers on "Eigenface published in 2021"


Proceedings ArticleDOI
27 Jan 2021
TL;DR: In this article, an approach similar to Eigenface is used for extracting facial features through facial vectors and the datasets are trained using Support Vector Machine (SVM) algorithm to perform face classification and detection.
Abstract: Today's pandemic situation has transformed the way of educating a student. Education is undertaken remotely through online platforms. In addition to the way the online course contents and online teaching, it has also changed the way of assessments. In online education, monitoring the attendance of the students is very important as the presence of students is part of a good assessment for teaching and learning. Educational institutions have adopting online examination portals for the assessments of the students. These portals make use of face recognition techniques to monitor the activities of the students and identify the malpractice done by them. This is done by capturing the students' activities through a web camera and analyzing their gestures and postures. Image processing algorithms are widely used in the literature to perform face recognition. Despite the progress made to improve the performance of face detection systems, there are issues such as variations in human facial appearance like varying lighting condition, noise in face images, scale, pose etc., that blocks the progress to reach human level accuracy. The aim of this study is to increase the accuracy of the existing face recognition systems by making use of SVM and Eigenface algorithms. In this project, an approach similar to Eigenface is used for extracting facial features through facial vectors and the datasets are trained using Support Vector Machine (SVM) algorithm to perform face classification and detection. This ensures that the face recognition can be faster and be used for online exam monitoring.

32 citations


Journal ArticleDOI
TL;DR: In this paper, the authors proposed a hybrid classical-quantum control, where parameterized quantum circuits are optimized with simple measurement observables, which significantly reduces the experimental complexity. And they implemented a human face recognition process using the images from the Yale Face Dataset.
Abstract: Principal component analysis (PCA) is a widely applied but rather time-consuming tool in machine learning techniques. In 2014, Lloyd, Mohseni, and Rebentrost proposed a quantum PCA (qPCA) algorithm [Lloyd, Mohseni, and Rebentrost, Nat. Phys. 10, 631 (2014)NPAHAX1745-247310.1038/nphys3029] that still lacks experimental demonstration due to the experimental challenges in preparing multiple quantum state copies and implementing quantum phase estimations. Here, we propose a new qPCA algorithm using the hybrid classical-quantum control, where parameterized quantum circuits are optimized with simple measurement observables, which significantly reduces the experimental complexity. As one important PCA application, we implement a human face recognition process using the images from the Yale Face Dataset. By training our quantum processor, the eigenface information in the training dataset is encoded into the parameterized quantum circuit, and the quantum processor learns to recognize new face images from the test dataset with high fidelities. Our work paves a new avenue toward the study of qPCA applications in theory and experiment.

17 citations


Journal ArticleDOI
27 Apr 2021
TL;DR: Computers show a significant difference among the three FR techniques in terms of overall time complexity and accuracy, and LBPH outperforms the other two FR algorithms on both LUDB and 5_Celebrity datasets by achieving 40% and 95% accuracy, respectively.
Abstract: Facial recognition (FR) in unconstrained weather is still challenging and surprisingly ignored by many researchers and practitioners over the past few decades. Therefore, this paper aims to evaluate the performance of three existing popular facial recognition methods considering different weather conditions. As a result, a new face dataset (Lamar University database (LUDB)) was developed that contains face images captured under various weather conditions such as foggy, cloudy, rainy, and sunny. Three very popular FR methods—Eigenface (EF), Fisherface (FF), and Local binary pattern histogram (LBPH)—were evaluated considering two other face datasets, AT&T and 5_Celebrity, along with LUDB in term of accuracy, precision, recall, and F1 score with 95% confidence interval (CI). Computational results show a significant difference among the three FR techniques in terms of overall time complexity and accuracy. LBPH outperforms the other two FR algorithms on both LUDB and 5_Celebrity datasets by achieving 40% and 95% accuracy, respectively. On the other hand, with minimum execution time of 1.37, 1.37, and 1.44 s per image on AT&T,5_Celebrity, and LUDB, respectively, Fisherface achieved the best result.

15 citations


Journal ArticleDOI
31 Mar 2021
TL;DR: It is proposed to pay attention to the amount of training data and the number of eigenvectors used to get the best level of accuracy.
Abstract: To find out if an employee is present, attendance is usually used. Attendance can be done in several ways, one of which is by filling in the attendance list that has been provided (manual attendance). However, this method is less effective because there is a possibility that employees who are not present will entrust attendance to employees who are present. Therefore, other ways are needed so that this does not happen. In this study, attendance was carried out using facial recognition. Face recognition is one of the fields used to recognize someone. A person's face usually has special characteristics that are easily recognized by people. These special characteristics are also called features. In this study, these features can be searched using the Principle Component Analysis (PCA) method. The PCA method is one of the methods used to produce features by reducing dimensions using eigenvectors from facial images (eigenface). The facial image used in this study consisted of 40 people with each person having 10 facial images with various expressions. Image data is divided into two parts, namely training data and test data. In this study, it is proposed to pay attention to the amount of training data and the number of eigenvectors used to get the best level of accuracy. From the research results, the highest level of accuracy occurs when the training data for each person is 7 and the test data for each person is 3 with an accuracy rate of 96.67%.

7 citations


Journal ArticleDOI
TL;DR: The main objective of this paper is to investigate and develop a real-time face recognition system for the attendance system based on the current scenarios, which consists of face detection, mask detection, face recognition, and attendance report generation modules.
Abstract: Face recognition is gaining popularity as one of the biometrics methods for an attendance system in an organization. Due to the pandemic, the common face recognition system needs to be modified to meet the current needs, whereby facemask detection is necessary. The main objective of this paper is to investigate and develop a real-time face recognition system for the attendance system based on the current scenarios. The proposed framework consists of face detection, mask detection, face recognition, and attendance report generation modules. The face and facemask detection is performed using the haar cascade classifier. Two techniques for face recognition were investigated, the eigenfaces and local binary pattern histogram. The initial experimental results and implementation at Kuching Community College show the effectiveness of the system. For future work, an approach that is able to perform masked face recognition will be investigated.

7 citations


Journal ArticleDOI
TL;DR: In this article, a face recognition system that uses 3D lighting estimation and optimal lighting compensation for dark-field application is proposed, which can realize people identification in a near scene dark field environment, a light-emitting diode (LED) overhead light, eight LED wall lights, a visible light binocular camera, and a control circuit are used.
Abstract: A face recognition system that uses 3-D lighting estimation and optimal lighting compensation for dark-field application is proposed. To develop the proposed system, which can realize people identification in a near scene dark-field environment, a light-emitting diode (LED) overhead light, eight LED wall lights, a visible light binocular camera, and a control circuit are used. First, 68 facial landmarks are detected, and their coordinates in both image and camera coordinate systems are computed. Second, a 3-D morphable model (3DMM) is developed after considering facial shadows, and a transformation matrix between the 3DMM and camera coordinate systems is estimated. Third, to assess lighting uniformity, 30 evaluation points are selected from the face. Sequencing computations of LED radiation intensity, ray reflection luminance, camera response, and face lighting uniformity are then carried out. Ray occlusion is processed using a simplified 3-D face model. Fourth, an optimal lighting compensation is realized: the overhead light is used for flood lighting, and the wall lights are employed as meticulous lighting. A genetic algorithm then is used to identify the optimal lighting of the wall lights. Finally, an Eigenface method is used for face recognition. The results show that our system and method can improve face recognition accuracy by >10% compared to traditional recognition methods.

6 citations


Proceedings ArticleDOI
30 Jul 2021
TL;DR: In this paper, a projected elegant reflector is proposed to make an individual's life comfortable, secure, sophisticated, etc, which identifies individuals based on identity verification and voice command.
Abstract: The motive behind this elegant reflector is to make an individual's life comfortable, secure, sophisticated, etc, . Projected Elegant reflector identifies individuals based on Identity verification and Voice Command. Here the elegant reflector can exhibit real time strategy like period, day, weather forecasting and news cast, to the individuals whenever they are in need of it, they can fetch the real time data by using the voice command inputs and identity verification. In this project we are using smart mirrors along with face recognition and speech recognition. Face recognition Algorithm using eigenface based algorithm. In this project, we are implementing home technology using software bots, which are controlled by our voice commands. With Google assistant we can ask any question and get an answer for it. It is dense and economical.

4 citations


Journal ArticleDOI
01 Jun 2021
TL;DR: This paper proposes and implements a novel approach for facial recognition in the encrypted domain that exploits the homomorphic properties of the Paillier cryptosystem and performs Euclidean distance calculations using encrypted data.
Abstract: 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.

4 citations


Journal ArticleDOI
01 Jan 2021
TL;DR: In this study, a personal data security system was built using Android-based deep learning and it is known that by using the eigenface method, the farther the face is from the camera, the face cannot be detected.
Abstract: The development of technology in security systems combined with facial recognition, of course, makes every protected data safe. Many methods can be combined with a security system, one of which is the eigenface method, which is part of facial recognition. In this study, a personal data security system was built using Android-based deep learning. Based on the results of tests carried out on three devices with different Android versions, it is known, if on Android 8.1 (Oreo) the maximum distance is ± 40 cm, on Android 9.0 (Pie) the maximum distance is ± 50 cm, and on the Android version, 10.0 (Q) the maximum distance for facial object recognition is ± 60 cm. From the test results, it is known that by using the eigenface method, the farther the face is from the camera, the face cannot be detected. The implementation of this system is expected to protect personal data safely. Keywords: Face recognition, Deep Learning, Android, Eigenface.

3 citations


Book ChapterDOI
01 Jan 2021
TL;DR: A comparative study of previous works of several authors on face recognition-based attendance management systems have been discussed, and a comparative study has been performed between the works as discussed by the authors, showing that the need for cost-effective and network independent face recognition based attendance system having high accuracy still exists.
Abstract: Face detection and recognition methods have enhanced a lot in the last decade in terms of accuracy, speed, and overall performance. This development has completely changed many systems. Attendance management is one example of this change. Earlier, the pen-paper-based method was used for marking and storing attendance. The improvement in biometric detection and recognition methods has resulted in the possibility to build a complete automated attendance management system. Face recognition based methods for attendance management has many advantages over the traditional attendance methods. The main challenge with the face recognition based method for attendance is to ensure that it recognizes every detected face. In this paper, previous works of several authors on face recognition based attendance management systems have been discussed, and a comparative study has been performed between the works. This paper shows that the need for cost-effective and network independent face recognition based attendance system having high accuracy still exists.

2 citations


Journal ArticleDOI
TL;DR: This paper presents a comparison of the identification rate and behavior of six recognizers against traditional morphing attacks, in which only two subjects are used to create the altered image: the original subject and the target, and presents a new morphing method that works as an iterative process of gradualTraditional morphing, combining the originalsubject with all the subjects’ images in a database.
Abstract: Face identification is becoming a well-accepted technology for access control applications, both in the real or virtual world. Systems based on this technology must deal with the persistent challenges of classification algorithms and the impersonation attacks performed by people who do not want to be identified. Morphing is often selected to conduct such attacks since it allows the modification of the features of an original subject’s image to make it appear as someone else. Publications focus on impersonating this other person, usually someone who is allowed to get into a restricted place, building, or software app. However, there is no list of authorized people in many other applications, just a blacklist of people no longer allowed to enter, log in, or register. In such cases, the morphing target person is not relevant, and the main objective is to minimize the probability of being detected. In this paper, we present a comparison of the identification rate and behavior of six recognizers (Eigenfaces, Fisherfaces, LBPH, SIFT, FaceNet, and ArcFace) against traditional morphing attacks, in which only two subjects are used to create the altered image: the original subject and the target. We also present a new morphing method that works as an iterative process of gradual traditional morphing, combining the original subject with all the subjects’ images in a database. This method multiplies by four the chances of a successful and complete impersonation attack (from 4% to 16%), by deceiving both face identification and morphing detection algorithms simultaneously.

Journal ArticleDOI
25 Feb 2021-Entropy
TL;DR: In this article, a transform domain steganography technique is proposed to hide a message in components of linear combination of high order eigenfaces vectors, which are responsible for dimensions with low amount of overall image variance.
Abstract: In this paper we propose a novel transform domain steganography technique—hiding a message in components of linear combination of high order eigenfaces vectors. By high order we mean eigenvectors responsible for dimensions with low amount of overall image variance, which are usually related to high-frequency parameters of image (details). The study found that when the method was trained on large enough data sets, image quality was nearly unaffected by modification of some linear combination coefficients used as PCA-based features. The proposed method is only limited to facial images, but in the era of overwhelming influence of social media, hundreds of thousands of selfies uploaded every day to social networks do not arouse any suspicion as a potential steganography communication channel. From our best knowledge there is no description of any popular steganography method that utilizes eigenfaces image domain. Due to this fact we have performed extensive evaluation of our method using at least 200 000 facial images for training and robustness evaluation of proposed approach. The obtained results are very promising. What is more, our numerical comparison with other state-of-the-art algorithms proved that eigenfaces-based steganography is among most robust methods against compression attack. The proposed research can be reproduced because we use publicly accessible data set and our implementation can be downloaded.

Journal ArticleDOI
01 Jun 2021
TL;DR: In this paper, the accuracy of low-quality facial recognition can be improved by processing the image for its feature extraction using two-directional matrix on 2D-PCA.
Abstract: Face recognition is a technique that can be used to distinguish the characteristic facial patterns of a person. A very influencing factor in the facial recognition process is image quality, so it can affect the level of accuracy. Improving the accuracy of low-quality facial recognition can be done by processing the image for its feature extraction using Two Directional Matrix on 2D-PCA. From the extraction process, the Eigenfaces value is then generated to be classified. Image classification is done by using Euclidean Distance. Furthermore, the accuracy results between image recognition with or without Two Directional Matrix will be compared. The data used were the AT&T face of database and database of Essex. Of the 18 tests carried out on the Two Directional Matrix method, it was proven to improve facial recognition accuracy by as many as 12 trials. In 5 other experiments the accuracy decreased and 1 experiment was unable to increase or decrease facial recognition accuracy. The highest accuracy result in the experiment using AT&T was 98.25%, while in Essex it was 97.27%. Suggestions for further research by conducting experiments with more diverse dataset ratios in order to get better low-quality image accuracy results.

Proceedings ArticleDOI
11 Jun 2021
TL;DR: In this article, a complete system which can be used with mobile platforms has been achieved with the proposed procedure, which is based on neural networks and eigenface approach and can be implemented on mobile robots as well.
Abstract: Due to the COVID-19 pandemic, the face masks are mandatory everywhere so it might be beneficial to automatize the detection and tracking of whether people wear a mask or not with the help of the computer vision. Furthermore, it can be implemented on mobile robots as well. Additionally, face recognition of people is discussed with two different techniques which are based on neural networks and eigenface approach in this study. Hence, a complete system which can be used with mobile platforms has been achieved with the proposed procedure.

Journal ArticleDOI
19 Jul 2021
TL;DR: 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 and to try to research the detection of facial recognition that is present in the application system.
Abstract: 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%.

Journal Article
TL;DR: In this paper, a study of different CNN-based facial expression recognition techniques is presented, including Local Binary Patterns, Eigenfaces, Facelandmark features, and Texture features.
Abstract: In many automated device applications, such as robotics education, artificial intelligence, and Defence, recognition of facial expressions plays a major role. It is difficult to precisely recognize facial expressions. Approaches to solve the problem of FER (Facial Expression Recognition) can be divided into 1) single static images and 2) image sequences. Similar methods, historically, Researchers used the Multi-layer Perceptron Model, kNearest Neighbours, Help Vector Machines to solve FER. Features such as Local Binary Patterns, Eigenfaces, Facelandmark features, and Texture features were derived from these methods. Among all these strategies, Neural Networks have gained a lot of popularity and are commonly used for Oh. FER. Due to their casual architecture and ability to provide good results without the need for manual feature extraction from raw image data, CNNs (Convolutionary Neural Networks) have recently gained popularity in the field of deep learning. This paper focuses on a study of different CNN-based facial expression recognition techniques. It involves state-of-the-art techniques proposed by various researchers. The paper also illustrates the steps needed for FER to use CNN. This paper also provides an overview of methods focused on CNN and problems that need focus when selecting CNN to solve FER

Book ChapterDOI
01 Jan 2021
TL;DR: In this paper, an efficient attendance management system is proposed, which makes use of dimensionality reduction using PCA-based face recognition algorithm incorporating Euclidean distance as the distance classifier.
Abstract: Student class attendance record plays an important role in most of the universities of Asia particularly countries like India. Keeping this in mind, an efficient attendance management system is proposed in this paper. This system makes use of dimensionality reduction using PCA-based face recognition algorithm incorporating Euclidean distance as the distance classifier. Faces of the students are segmented using Viola–Jones algorithm. Based on the recognized faces, attendance is updated into an Excel database. The algorithm has been tested using two different student databases. Training database is created using 50 subjects including male and female with different facial variations (15 instances per subject). Statistical data shows that an accuracy of the algorithm is greater than 99% with normal lighting conditions.

Journal ArticleDOI
27 Aug 2021
TL;DR: After some experiment, it was proven that the system designed is accurate, reliable and safe enough to be implemented to digital authorization process.
Abstract: Facial recognition is one of the most popular way to authenticate user into a system. This method is preferable considering the tendency of users for using the same password across multiple sites which made the user has already made his own account securities in vulnerable states. Using biometrics might supply solutions to solve this problem and facial recognition is one of the best biometric methods can be apply as a digital account security solution. This study to design a prototype system implementing facial recognition to verify users to measure how accurate these methods are. The method used here is Viola-Jones for face detection, Eigenface and Haar feature for face recognition from the OpenCV. The system was designed in Java. Based on the test results from the system designed, system can recognize user face with 100% accuracy if faces are shot in a well desirable condition. The system is able to recognize the user's face with various expressions including with or without glasses. However, the system has difficulty in recognizing user’s face in facing up, down, sideways position or blocked by accessories or body parts such as hands. After some experiment, it was proven that the system designed is accurate, reliable and safe enough to be implemented to digital authorization process.

Proceedings ArticleDOI
19 May 2021
TL;DR: In this paper, Haar classifiers are used for face detection and Eigenfaces face recognition, which produces an accuracy of around 99.4% for still images and 98.6% for video recordings.
Abstract: One of the most common way of verification is ‘Face Recognition’ which can also be implemented for communication between machines and humans. In industries, corporate sectors, financial exchanges etc., face detection and recognition are used for the purpose of authentication. Another area of interest is the communication of information securely. In this paper, for face detection process Haar-classifiers are used which produces an accuracy of around 99.4% for still images and 98.6% for video recordings. Using two algorithms i.e. LBPH algorithm and Eigenfaces face recognition is done. The former produces a recognition accuracy of 99.39% for still images and 99.32% for video recordings and latter produces accuracies of 99.28% and 98.87% for the same. The paper also proposes a more secure modified AES, whose modification done by 43 variable S - Box generation out of which 9 S - Box return correct inverse S – Box with the help of variable irreducible polynomial in GF (28 ) finite field. Thus every time using the variable irreducible polynomial in GF (28 ) a new S- Box and inverse S-Box is generated which makes it difficult for the intruders to crack modified AES.

Proceedings ArticleDOI
01 Jul 2021
TL;DR: In this article, a real-time approach for the identification of faces in a security camera induced video stream is proposed, which is essentially to apply and validate the recognition algorithm Eigenfaces which uses principal component analysis in order to solve the recognition problem of 2D faces.
Abstract: Real time face recognition has been used to implement a real-time approach for the identification of faces in a security camera induced video stream. Consequently, both facial identification and facial recognition techniques are briefly listed and related technical data are not omitted. The solution proposed was essentially to apply and validate the recognition algorithm Eigenfaces which uses principal component analysis in order to solve the recognition problem of two-dimensional faces. The snapshots reflecting the input images of your device are projected onto a face space (characteristic space) that explains better the variance for the set of facial images. The ‘owners,’ which are the characteristic vectors of the face set, define the face space. These elements help to rebuild a fresh face image that has been projected into face space with considerable details (named weight). To identify the human being, in this feature space the current picture projection is compared to the available projections from the training array. The PCA technique is used since it is effective in developing facial recognition systems. The frame will conduct real-time facial detection and analysis as well as provide feedback in the form of a window showing the details from the database of the topic and send an e-mail to interested organisations.