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This paper aims at developing an intelligent face recognition students' attendance system to enhance school attendance tracking. The system is made up of four phases-database of students' details, face detection, face recognition and attendance report. The database stores all the students' details and images of the students' face captured while face detection and recognition is carried out with convolutional neural network algorithms from the face recognition and opencv library. As faces are detected and recognized from live streaming video of the classroom, attendance are recorded into an excel file and then sent to a real time database. The system also contains a mobile interface through which the course instructors can access information at all time. The methodology adopted for this work is object-oriented analysis and design methodology (OOADM) while programming language used is Python. This work has helped immensely to eliminate the issue of proxy attendance as well as reduce the time wasted in manual attendance system. It is very beneficial to schools and other institutions where attendance is required. [ABSTRACT FROM AUTHOR]

Edge computing (EC) gets the Internet of Things (IoT)-based face recognition systems out of trouble caused by limited storage and computing resources of local or mobile terminals. However, data privacy leak remains a concerning problem. Previous studies only focused on some stages of face data processing, while this study focuses on the privacy protection of face data throughout its entire life cycle. Therefore, we propose a general privacy protection framework for edge-based face recognition (EFR) systems. To protect the privacy of face images and training models transmitted between edges and the remote cloud, we design a local differential privacy (LDP) algorithm based on the proportion difference of feature information. In addition, we also introduced identity authentication and hash technology to ensure the legitimacy of the terminal device and the integrity of the face image in the data acquisition phase. Theoretical analysis proves the rationality and feasibility of the scheme. Compared with the non-privacy protection situation and the equal privacy budget allocation method, our method achieves the best balance between availability and privacy protection in the numerical experiment. [ABSTRACT FROM AUTHOR]

Using biometric technology for recording attendance in the school environment is still not widely done by researchers. In this study, a solution was proposed to the problems that occurred in the school environment where parents/guardians could not monitor the presence of their children in school. The solution offered is a student attendance recording system based on facial recognition algorithms (face recognition). The built system can record the presence of students when entering the classroom and when returning home or out of class. Proposed methods for identifying student attendance are the Haar Cascade and Eigenface algorithms. The system can also provide notice of attendance or absence of students in real time to parents/guardians via email that has been registered. The method can provide accurate and fast facial recognition results based on the test results. The presence system developed based on mobile can recognize faces up to a distance of 200-300 cm with low and moderate light intensity.

Many people and children in the world go missing every day, which the police and local authorities must deal with. The police and municipal authorities deal with many missing persons and kid cases every day. In 2020, National Crime Records Bureau (NCRB) listed around 300 thousand missing persons in India. The general process normally deals with investigation, which requires experience and can be time-consuming if the missing person is migrated. The overall procedure usually entails inquiry, which is time-consuming and requires experience. Our research intends to create an AI system that can identify and evaluate a person's facial features and then compare them to a face in the database, even if they are located years after going missing. Using the Yale Face Dataset on Kaggle containing over 70,000 images for the model, the training of such large dataset improved the model for the Eigenface as well as the GAN algorithm with the use of OpenCV and HaarCascade library helped improve and enhance the accuracy with the result of near 99. [ABSTRACT FROM AUTHOR]

Effective monitoring of the tool wear condition within a machining process can be very challenging. Depending on the sensors used, often only a part of the relevant wear information can be detected. In the case of milling processes data acquisition is made even more difficult by the fact that the process working point is inaccessible for sensor applications due to the physical tool, the machining process itself, the chipping and used cooling-lubricants. By using a variety of sensors and different measuring principles, sensor data fusion strategies can counteract this problem. An approach to this is the eigenface algorithm. This approach, a face recognition technique, is tested for its suitability on tool condition monitoring in milling processes by using multi-sensor process data. [ABSTRACT FROM AUTHOR]

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 hardware, also the model is of weak interpretability, which makes 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 Principal Component Analysis method first before the eigenface 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 the 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 is also 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. [ABSTRACT FROM AUTHOR]

This study aimed to apply face recognition of the personal face detection, which this system implemented Eigenface to analyze face details in facial comparison database. Eigenface is an approach in the theory of principal component analysis (PCA). It was accepted that facial images could be synthesized based on data from the model and can store a person’s face parameters in a small set of numbers, which is accurate and reliable. The data used in the assessment of effi ciency and developed software, including 30 images. It was divided into three groups, and each group contained ten images. The experimental results were shown as follows ; the fi rst dataset contained ten images with an image resolution of 100x100 pixels. We achieved a precision of 90%. The second dataset also contained ten images with an image resolution of 150x150 pixels. The result showed that we achieved a precision of 80%. The third dataset included ten images with a resolution of 200x200 pixels. We achieved a precision of only 70%. Our proposed system achieved a mean precision of 80% and was considered as good efficiency. It can be applied for application in criminal face detection.

• We propose a randomness-based privacy-preserving face recognition scheme. • We design a novel face encryption algorithm to protect the privacy of face images. • We propose an optimization technique to increase the efficiency of face owner and the cloud. Face recognition (FR) has become increasingly significant in many computer vision applications. However, with the rapid deployment of FR, the privacy of face images has been a growing concern, especially when FR is performed in cloud platforms. Several privacy-preserving face recognition (PPFR) schemes have been proposed to outsource FR without divulging private information. However, most existing schemes are built by employing partially or fully homomorphic cryptosystems, which contain a series of computationally expensive operations and require interactions between users and service providers. In this paper, we propose a novel and efficient scheme to achieve privacy-preserving face recognition in the cloud. Using randomness techniques, we propose an affine transformation, which consists of permutation, diffusion and shift transformations, to protect the privacy of faces. Both projection (feature extraction) and recognition procedures are performed in encrypted domain without interaction. We also propose an optimization technique to increase the efficiency of encryption. Extensive experimental analysis is performed to demonstrate the correctness and effectiveness of the proposed scheme. Theoretical comparison is also conducted to illustrate the efficiency of the proposed scheme. [ABSTRACT FROM AUTHOR]

Attendance is the documentation of presence and activity in institution. A software has been made to monitor the attendance using face recognition. The software uses camera to capture the image and works on any background color. The aim of this paper is to calculate its performance with sensitivity, specificity, and accuracy using Eigenface Algorithm and Principal Component Analysis (PCA) method. Face recognition in this paper is based on Eigenface algorithm, using pixel information from images captured by webcam. The image is represented using PCA method. The software is tested using different expressions and accessories in object’s face. The performance of the software indicates 73.33%sensitivity, 52.17% specificity, and 86.67% accuracy. The successful rate in identifying the face for distance testing is 70%, while successful rate of 85% is achieved for object wearing eyeglasses and veil (jilbab). Furthermore, the successful rate for various expression is 85.33%.

Authentication is the process of verifying one’s identity, and one of its implementation is in taking attendances in university’s lectures. Attendance taking is a very important matter to every academic institution as a way to examine students’ performance. Signature based attendance taking can be manipulated. Therefore it has problems in verifying the attendance validity. In this final project, a real time eigenface based face recognition is implemented in an application to do attendance taking. The input face image is captured using a webcam. The application itself is built in C#, utilizing EmguCV library. The application is developed using Visual Studio 2015. Face detection is done with Viola-Jones algorithm. The eigenface method is used to do facial recognition on the detected face image. In this final project, a total of 8 testings are done in different conditions. From the testings, it is found that this application can recognize face images with accuracy as high as 90% and as low as 6.67%. This solution can be used as an alternative for real-time attendance taking in an environment with 170 lux light intensity, webcam resolution of 320 x 240 pixel, and the subject standing 1 meter away while not wearing spectacles. The average recognition time is 0.18125 ms.

Human face finding and tracing is a technique to define whether a human face is existing or not in a framing image. Different from human face recognition which discriminates dissimilar faces, face discovery simply designates whether a human face is contemporary in a framing image or not. Face following determines the particular position or movement of human faces. The paper describes some famous and important algorithms and techniques which are used for face detection, tracing and recognition. Human face detection and recognition is the most important space of biometrics system (Muhammet and Resul, 2013). Biometric techniques are frequently used for substantiation process to isolate fingerprint, eye iris, retina, etc. The paper evaluates numerous procedures and methods for the fruitful detection, tracking and recognition of human face. [ABSTRACT FROM AUTHOR]

Automated facial recognition methods have become widely used as a way to ascertain the identity of individuals. Yet the methods by which facial recognition technologies (FRT) operate – the machinic performance of the perception of the human face – are often invisible to those under their gaze. This article investigates the machinic perception of the face through an FRT method known as eigenface, in order to both reveal and problematize the ways of seeing that underlie it. As part of its algorithmic processes, eigenface produces an image. This image can be understood as a portrait of machine recognition, making visible the processes through which the algorithm performs recognition and 'sees' the human face. The eigenface portrait reveals a way of seeing that is based on statistical processes of pattern recognition. An analogue antecedent of this application of statistics to the recognition of facial images can be found in the composite portrait. Through a dialectical discussion of composite portraiture in multiple disciplinary fields ranging from sociology to philosophy and the visual arts, this article experiments with providing a cultural and social translation of machine processes of visual perception. The discussion shifts the focus of enquiry towards the aesthetics of the algorithmic process in order to provide an entry point for critique and a possible reimagination on algorithmic knowledge production. [ABSTRACT FROM AUTHOR]

The assumption that the number of training samples is less than the number of pixels in a face image is essential for conventional eigenface-based face recognition. But recently, it has become impractical for massive face image collections. A parallel processing method using distributed eigenfaces is presented. A massive face image set was divided into a bunch of small subsets that satisfied the assumption of conventional approaches. Eigenfaces were extracted from the subsets and stored in a cloud system. Face recognition was performed by parallel processing using the distributed eigenfaces in the cloud system. A face recognition system was implemented in the Hadoop system. Various experiments were performed to test the validity of the distributed eigenface-based approach. The experimental results show that, compared to conventional methods, the implemented distributed face recognition system worked well for large datasets without significant performance degradation. [ABSTRACT FROM AUTHOR]

Proses absensi yang dilakukan secara manual dinilai kurang efektif karena terbukanya kesempatan melakukan kecurangan. Selain itu, proses rekapitulasi manual membutuhkan waktu yang lama. Sistem absensi dengan teknologi dapat diterapkan untuk membantu proses absensi dan rekapitulasi yang efektif. Pada tugas akhir ini, teknologi yang digunakan adalah sistem pengenalan wajah. Pembuatan aplikasi absensi dengan pengenalan wajah ini menggunakan metode Eigenface untuk melakukan proses pengenalan wajah. Sedangkan data-data yang dibutuhkan sistem adalah data mata kuliah, dosen, jadwal, kelas, mahasiswa, dan dataset foto wajah yang disimpan dalam sistem manajemen relasional basis data. Hasil dari aplikasi yang dibangun yaitu dapat mengelola data-data pada sistem, serta melakukan pencatatan dan perekapan data absensi. Proses absensi mahasiswa berhasil dilakukan pada kondisi pencahayaan yang bagus dan resolusi yang sama dengan kondisi foto wajah yang disimpan dalam basis data.

Pengenalan wajah manusia (face recognition) merupakan salah satu bidang yang cukup berkembang dewasa ini. Berbagai aplikasi dari alat dengan kemampuan seperti ini terbentang luas dari pencarian penjahat, kriminalitas, sistem akses keruangan, sampai interaksi manusia dengan komputer. Aplikasi ini menggunakan sebuah kamera yang ada pada smartphone android untuk menangkap wajah seseorang kemudian dibandingkan dengan wajah yang sebelumnya telah disimpan dan dilatih di dalam database. Jika hasil tangkapan kamera cocok dengan identitas wajah pada database, maka identifikasi wajah berhasil, jika tidak cocok maka akan dinyatakan gagal. Algoritma pengenalan wajah yang digunakan adalah algoritma eigenface yang berasal dari Library OpenCv, library tersebut sudah dapat digunakan untuk mengidentifikasi wajah seseorang. Hasil pemrosesan pengenalan wajah dengan menggunakan metode eigenface pada OpenCv ini dididapatkan sangat sensitif, karena bergantung pada pencahayaan, jarak antara subjek dan kamera, sudut pandang wajah, ekspresi/mimik wajah, pemakaian aksesoris, perubahan latar (background).Â