Face Recognition Based Attendance System Using Machine Learning and Euclidean Distance

This paper introduces a cutting-edge face recognition-based attendance system, designed to address the limitations of traditional attendance methods through the integration of advanced machine learning, computer vision, and geospatial APIs. The system streamlines the attendance process by automating the identification and logging of attendees with high accuracy and efficiency. Key features include live video recognition for real-time face identification, an intuitive user registration module for enrolling new individuals, CSV-based logging for seamless data export and management, and geolocation-aware attendance tracking to ensure that records are not only time-accurate but also location-specific. This geospatial context provides valuable insights, particularly for distributed teams or multi-location setups. The implementation leverages Python, a versatile programming language, and integrates OpenCV for real-time video processing and face detection, ensuring quick and reliable face recognition even in dynamic environments. The graphical user interface (GUI) is developed using PyQt5, allowing for a user-friendly and responsive experience. This combination of powerful technologies ensures that the system is both scalable and adaptable, able to integrate easily into various organizational workflows, from small educational institutions to large-scale corporate environments. The system's practical application is validated through experimental results conducted in diverse settings, including workplaces, academic institutions, and security-sensitive environments. These results highlight the system’s exceptional accuracy, even under challenging conditions such as low lighting or crowded spaces. Furthermore, the system demonstrates its potential to enhance operational efficiency, reduce administrative overhead, and improve security by providing a reliable, context-aware solution for attendance management. In conclusion, this face recognition-based attendance system offers a modern, automated solution that combines the power of machine learning and computer vision with geospatial data, creating an intelligent, highly effective tool for attendance tracking across a wide range of industries and applications.

Objectives: Face recognition has arisen as a smart solution to discourse many present-day needs for empathy and the confirmation of identity claims. It fetches together the capacity of other biometric methods, which endeavor to tie uniqueness for the individual characteristic features of the body, and the further familiar functionality of visual reconnaissance systems. Face acknowledgment is a vital field for authentication purpose particularly in the case of student's attendance. This paper is intended at applying a digitized system for attendance recording. Methods/Statistical Analysis: In this process two methods are used to determine the face recognition attendance system- Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA). PCA is a statistical procedure that uses an orthogonal modification to transform a set of observations of probably correlated variables into a set of values of linearly uncorrelated variables. LDA is a generalization of Fisher's linear discriminant, a method used in statistics, pattern recognition and machine learning to find a linear permutation of features that shows or separates two or more classes of objects or events. Findings: However the current system of tracking attendance via records is difficult to manage. Face recognition based attendance system deals with the maintenance of the student's daily attendance details. Biometric appreciation comprises alike, within an open-mindedness of calculation, of pragmatic biometric behaviors in contradiction of formerly poised data for a focus. Estimated identical is obligatory due to the dissimilarities in biological characteristics and deeds both within and among persons. It generates the attendance for the student on basis of presence in the class. Application/Improvements: Project can be modernized in nearby future as, when a responsibility for the same arises, as it is very flexible in positions of growth. And the enrichment approach of camera formation based on the result of the position valuation in order to progress the face detection effectiveness.

The purpose of this research is to design and implement an attendance system based on internet of things (IoT) . The proposed system integrated two types of attendance systems, face recognition based attendance system (FRA) and fingerprint-based attendance system (FPA), with a central server. The FRA was developed in a Raspberry Pi mini-computer using Python programming language and Openc CV library. The FPA, on the other hand, was developed using Node MCU ESP8266 and Fingerprint scanner AS608 with Adafruit Fingerprint library. Both FRA and FPA are connected to a web server with a database engine through the internet connection and sensing attendance data using the HTTP_POST method. The server was developed using Apache Webserver, PHP programming language, and MySQL database engine. The server serves two main purposes, which are to record the attendance data sent by the FPA and FRA, and generate an attendance report based on the user query. The system testing was done in a local network. The result showed that both the subsystems and the integrated system worked well

Traditional approach for attendance in schools and colleges is professor calls the name or roll no. of students and record the attendance. The manual work included in the maintenance and management of the traditional attendance sheets is difficult and is a tedious work. This paper presents a system that automatically identifies and recognizes the individual in a live captured image and marks the attendance for that person. It is a web-based application in which RetinaFace algorithm has been used to detect the face in face image. FaceNet algorithm then extracts features from the image of a person's face and SVM classifier classifies the face based on extracted features. The classifier used here has been trained with 128 dimension values of each face. The developed system exhibits 99.76% accuracy on training data and 97.21% accuracy on validation data with Parameters of SVM as C=100, kernel=poly, degree=5 and probability=True. Other classifiers, namely, Random Forest, KNN and Logistic regression were also used for referencing with Accuracy, but among those, SVM classifier provided the best result.

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.

This research aims to design and implement an Internet of Things (IoT) based attendance system. The designed system integrates two types of attendance systems, face recognition-based attendance system (FRA) and fingerprint-based attendance system (FPA), with a server. FRA uses Python programming language and Openc CV library, while FPA is developed using ESP8266 MCU Node and AS608 Fingerprint scanner with Adafruit Fingerprint library. FRA and FPA are connected to a web server with a database engine via an internet connection and send attendance data using the HTTP_POST method. The server development uses Apache Webserver, PHP programming language, and MySQL database engine. The server has two primary purposes: to record attendance data sent by FPA and FRA and to generate attendance reports based on user requests. System testing is done on a local network. The results show that the subsystems and integrated systems work well.

Aims: The rise of automation has greatly impacted various aspects of daily life, including attendance management systems. Traditional methods like manual roll calls and card swiping are often inefficient and prone to errors, leading researchers to explore innovative alternatives such as face recognition technology. This technology employs advanced algorithms and image processing techniques to accurately identify individuals based on their unique facial features, thereby enhancing accuracy and security measures. The objective of this project is to develop a face recognition-based attendance system with specific goals, including circuit design, simulation using Proteus Software, implementation, and performance evaluation through rigorous field-testing. Study Design: The system comprises an input subsystem with a Camera module and two output subsystems: an LCD and a Web-database PC software. Initially, custom and stored image data of students are saved in a microSD card of the camera module, and attendance records are updated on a web-database upon successful scanning and recognition of students' faces. Methodology: The implementation process involves assembling various hardware components, including securely mounting the ESP32 CAM and integrating a 16x4 Character LCD Display for visual feedback. LEDs are used as visual indicators for system statuses, while a Power Bank Module ensures consistent power supply. The system is controlled by the ESP32-CAM, which captures and verifies faces, displays actions on the LCD, and updates the web-database with attendance data. Results: Testing results indicate the system's high accuracy, achieving a 100% attendance rate across multiple class sessions with zero misclassifications. Conclusion: The user-friendly interface and seamless wireless connectivity enhance accessibility and real-time monitoring, making it a valuable tool for classroom management and office use. The research has some hardware limitations which includes processing large amounts of facial data requires robust hardware. Inadequate camera resolution or processing power can lead to slow performance or inaccurate recognition. Overall, the face recognition-based attendance system has the potential to revolutionize traditional methods, offering high accuracy, user-friendly features, and robust connectivity, with possible applications beyond education through further refinement and research. Facial recognition-based attendance systems are more accurate and efficient than traditional methods like manual roll calls or card swiping. By automating attendance, they reduce errors and provide real-time updates with web integration. Despite hardware limitations, the system's accuracy and ease of use make it a superior alternative with potential for broader applications.

Study question How can we automate the selection of oocytes for in vitro fertilization using meiotic spindle (MS) assessment? Summary answer Determining the Euclidean distance for MS characteristics and oocyte fertilization rate paves the way for data algorithmization, machine learning and further automation of oocyte selection. What is known already Oocyte selection is a crucial embryological step, particularly in regions with legal limitations on the number of female gametes used in assisted reproductive technology (ART) programs. MS assessment serves as a valuable non-invasive predictor of oocyte quality, but it remains prone to subjective interpretation by embryologists. Euclidean distance is typically used to quantify the similarity between two points based on the distance between them in n-dimensional space. However, this mathematical approach can be leveraged for data classification and machine learning. In our study, we calculated the Euclidean distance for MS characteristics and oocyte fertilization rate. Study design, size, duration Following oocyte denudation (n = 180), MS was via polarization microscopy based on three categories: morphology, orientation, and localization. The fertilization rate following intracytoplasmic sperm injection (ICSI) was recorded for each MS variant of the stated categories A three-dimensional coordinate system was constructed to map fertilization rates across different MS category combinations. The MS configurations with the shortest Euclidean distance to the highest fertilization rates were identified as optimal for IVF. Participants/materials, setting, methods This retrospective study analyzed donor oocyte cycles from 24 women aged 26.9 ± 2.3 years. Participants underwent controlled ovarian stimulation using exogenous gonadotropins in a short protocol for superovulation induction. Main results and the role of chance Our results indicate that oocytes with a spindle-shaped MS, positioned within a 45° range to the first polar body and within a 45° range, demonstrated the highest fertilization potential (p˂0.003 and p˂0.020). Conversely, oocytes with poorly visualized MS, located near the pole opposite to the first polar body exhibited the lowest fertilization rates (p˂0.008 and p˂0.003, respectively). The MS axis direction did have significant impact of the fertilization rate of fresh oocytes (p > 0.05). Euclidean distance calculations based on the fertilization index for each category variant facilitated the ranking of MS types to prioritize gametes for fertilization selection. Limitations, reasons for caution The study exclusively analyzed oocyte fertilization rates from young female donors, which may not be generalizable to older patients. Additionally, a larger dataset is required to refine MS classification boundaries. Wider implications of the findings Applying Euclidean distance to MS assessment improves objectivity in oocyte selection, reducing bias and enhancing reproducibility. This method refines gamete selection, boosting fertilization success and optimizing ART programs. AI-driven analysis with integrated Euclidean distance provides a scalable way to enhance embryological decision-making and IVF outcomes. Trial registration number No

Stubble burning is the most common practice in Northern region of India and is considered as the main reason for pollution in nearby NCR (National Capital Region) New Delhi, India. Therefore, in this research work, paddy stubble burnt fields were identified by using fuzzy based machine learning algorithms for a test site in Patiala, Punjab, India. An Euclidean Distance (ED) and Gaussian Kernel based Modified Possibilistic c-Means (MPCM) algorithms were used for this purpose. The main objective of this research work was to test the effectiveness of machine learning approach for identification of paddy stubble burnt filed and also to find out the better classifier amongst Euclidean Distance (ED) and Gaussian Kernel MPCM classifier. The Sentinel 2A/2B cloud free temporal data having a high frequency of repetitiveness in the interval of 5-days were used. To reduce spectral dimensionality of data Class Based Sensor Independent Indices (CBSII) database were generated. The burning was mainly observed from the last week of October to the first week of November and has been visualized using variation of CBSII-NDVI temporal plot. Normalized Burnt Ratio (NBR) was computed and compared with CBSII-NDVI outputs for both the ED and Kernel based MPCM classifiers. Delta Normalized Burnt Ratio (ΔNBR) was computed for cross-validation of burnt patches and is found to be in between 0.45 and 0.66. Thus, it can be concluded that kernel based MPCM classifiers are more efficient in terms of accuracy and robustness to noise in comparison to distance based MPCM classifiers, for the mapping of paddy burnt fields.

The principle component analysis(PCA) is a common feature extraction method in machine learning and pattern recognition approaches. PCA has been used in many applications, and face recognition in which specific faces are recognizing in an images database is one of the popular applications. The default distance metric which has been used with PCA based-face recognition is Euclidean distance. In this study, we have tested the Mahalanobis distance instead of Euclidean, and PCA based on Mahalanobis distance suggested a better performance on our students images database with highest recognition rate. However, we proposed weighted and normalized Mahalanobis distance based PCA-face recognition(PCA_WNMD). The proposed algorithm (PCA_WNMD) showed an improvement in faces recognition rate when tested on our students images database compared to PCA based on Mahalanobis and default Euclidean distances.

The Standardized Precipitation Index (SPI) is computed over CONUS using daily precipitation estimates from the NOAA Daily U.S. Climate Gridded Dataset (NClimGrid-Daily). From the NClimGrid-SPI (1951-present; 0.05°x0.05°), we derive historical hydro-climatological conditions and drought information from the Drought Severity and Coverage Index (DSCI) which combines drought levels into a single areal value (from 0 to 500). One of our objective is to better understand drought dynamic and particularly how drought episodes evolve from short term rainfall deficit (i.e., less than three months) to persistent drought condition (i.e., beyond nine months). To investigate how those cascading effect work, we use a Machine Learning (ML) approach to identify spatio-temporal patterns of drought episodes over CONUS. Several unsupervised ML clustering algorithms are tested using an ensemble of features including drought duration, rainfall accumulation, drought severity (maximum DSCI, time of maximum DSCI), seasonality (drought beginning and end dates), location (latitude, longitude). Results show that the most severe drought events (i.e., DSCI > 350) are those that have the longest durations and for which drought relief is associated with higher rainfall accumulation regardless of the location considered. Furthermore, there is an apparent consistency across accumulation scales and the number of parameters selected with an optimum number of clusters around four. The Euclidian distance ML models tested seems to be able to define spatiotemporal areas of similar drought patterns. Differences between models are observed in terms of spatial definitions and predominance of a cluster at a given location. The strongest prevalence of a given cluster has allowed to isolate areas of coherence such as the Pacific Northwest, the PNW, the Eastern Seaboard and the Southeast, and the Southwest area along the MX-US border. Domain delineations are weaker for areas such as the Rockies, the Midwest, and the Great Plains. While the SPI algorithm assumes a Gamma (McKee et al., 1993) or a Pearson III (Guttman, 1998) distribution for monthly rainfall accumulation periods, results show that this assumption might not be optimal depending on the domain considered and the accumulation period when applied to daily drought monitoring.

The problem of data representation on a sphere of unknown radius arises from various disciplines such as statistics (spatial data representation), psychology (constrained multidimensional scaling), and computer science (machine learning and pattern recognition). The best representation often needs to minimize a distance function of the data on a sphere as well as to satisfy some Euclidean distance constraints. It is those spherical and Euclidean distance constraints that present an enormous challenge to the existing algorithms. In this paper, we reformulate the problem as an Euclidean distance matrix optimization problem with a low rank constraint. We then propose an iterative algorithm that uses a quadratically convergent Newton-CG method at each step. We study fundamental issues including constraint nondegeneracy and the nonsingularity of generalized Jacobian that ensure the quadratic convergence of the Newton method. We use some classic examples from the spherical multidimensional scaling to demonstrat...

Despite the advances of deep learning in specific tasks using images, the principled assessment of image fidelity and similarity is still a critical ability to develop. As it has been shown that Mean Squared Error (MSE) is insufficient for this task, other measures have been developed with one of the most effective being Structural Similarity Index (SSIM). Such measures can be used for subspace learning but existing methods in machine learning, such as Principal Component Analysis (PCA), are based on Euclidean distance or MSE and thus cannot properly capture the structural features of images. In this paper, we define an image structure subspace which discriminates different types of image distortions. We propose Image Structural Component Analysis (ISCA) and also kernel ISCA by using SSIM, rather than Euclidean distance, in the formulation of PCA. This paper provides a bridge between image quality assessment and manifold learning opening a broad new area for future research.

We propose a novel method to improve the performance of existing three dimensional (3D) human face recognition algorithms that employ Euclidean distances between facial fiducial points as features. We further investigate a novel 3D face recognition algorithm that employs geodesic and Euclidean distances between facial fiducial points. We demonstrate that this algorithm is robust to changes in facial expression. Geodesic and Euclidean distances were calculated between pairs of 25 facial fiducial points. For the proposed algorithm, geodesic distances and 'global curvature' characteristics, defined as the ratio of geodesic to Euclidean distance between a pairs of points, were employed as features. The most discriminatory features were selected using stepwise linear discriminant analysis (LDA). These were projected onto 11 LDA directions, and face models were matched using the Euclidean distance metric. With a gallery set containing one image each of 105 subjects and a probe set containing 663 images of the same subjects, the algorithm produced EER=1.4% and a rank 1 RR=98.64%. It performed significantly better than existing algorithms based on principal component analysis and LDA applied to face range images. Its verification performance for expressive faces was also significantly better than an algorithm that employed Euclidean distances between facial fiducial points as features.

MOLECULAR TYPING OF KLEBSIELLA PNEUMONIAE USING IR BIOTYPER WITH MACHINE LEARNING: A PROMISING TOOL FOR EARLY OUTBREAK DETECTION