A Cost-Effective IoT-Driven Intruder Detection System Using Machine Learning Based Face Recognition | Chapter 1 | Mathematics and Computer Science: Contemporary Developments Vol. 6

The present study explores about Cost-Effective IoT-Driven Intruder Detection System using machine learning-based face recognition. An intruder may enter the premises without the owner's knowledge. A motion detection system using a PIR sensor is employed to detect any movement near the entrance. IoT security solutions to avoid theft require an intelligent security system that is convenient and requires minimum human effort. A USB camera is triggered to capture the intruder's image when motion is detected. This image is then processed using Machine Learning algorithms and OpenCV for face detection and recognition. The Raspberry Pi compares the detected face with a database of approved images. It processes 28 images per second and sends an email notification to the owner, indicating whether the person is authorized or unauthorized. The owner can verify the authentication via the Internet of Things.  The developed low-cost system is fast, highly accurate, gives efficient alerts, and serves as a monitoring system. It is convenient to solve security problems, which will help reduce or stop break-ins.

 

Author (s) Details

G. Mallikharjuna Rao
Department of ECE, Chaitanya Bharathi Institute of Technology, Hyderabad, India.

 

Haseena Palle
Department of ECE, Chaitanya Bharathi Institute of Technology, Hyderabad, India.

 

Pragna Dasari
Department of ECE, Chaitanya Bharathi Institute of Technology, Hyderabad, India.

 

Shivani Jannaikode
Department of ECE, Chaitanya Bharathi Institute of Technology, Hyderabad, India.

 

Please see the book here:- https://doi.org/10.9734/bpi/mcscd/v6/2466

An Efficient and Cost Effective Performance of Autonomous Attendance System Using Convolution Neural Networks | Chapter 1 | Research and Developments in Engineering Research Vol. 4

 This research work is proposed to developing a less turmoil, cost effective and robust exercise of autonomous attendance administration system. To watch carefully students' daily occupancy, marking attendance is a low practice for all educational organizations at all levels. There was once a opportunity when attendance was tracked manually. Although they are behind and labor-intensive for a large number of pupils, these approaches are exact and expel the possibility of deceptive enrollment. To address the disadvantages of manual systems, autonomous schemes based on high frequency recognition leafing through, fingerprint scanning, face acknowledgment, and iris scanning are being grown. Each technique has benefits and disadvantages. However, the majority of these systems are restricted by the necessity for individual human engagement all along the recording of attendance. To close the gaps in the current human and independent attendance management structures, we developed a healthy and efficient attendance recording order using a distinct group shot to detect face labeling and recognition algorithms. A extreme-definition camcorder mounted in a stable district records a series of countenances for each scholar seated in a classroom. The faces are therefore extracted from the group chance using a protocol, and they are identified using a convolutional interconnected system that is familiar to students from a face table. We evaluated our technique utilizing several datasets and group pictures. Our research shows that the submitted framework performs better in agreements of effectiveness, utility, and implementation than the current attendance pursuing methods. The suggested resolution is a self-contained attendance method that necessitates little contact middle from two points humans and machines, making integration into a smart school room simple.

Author(s) Details:

S. Purushothaman,
Department of Electronics and Communication Engineering, V.S.B Engineering College, Karur, Tamil Nadu, India.

Please see the link here: https://stm.bookpi.org/RADER-V4/article/view/10738

Development of Automated Attendance System Using Face Recognition | Chapter 8 | Current Overview on Science and Technology Research Vol. 6

 Attendance notes during classroom lectures are not only difficult but also time consuming. There is always the possibility of representative participation. Tracking attendance using traditional methods has proven difficult. In recent years, common biometrics such as fingerprints have been used to overcome the problem of automatic presence her marking. Instead of using traditional methods, the proposed solution aims to create an automated system that records the presence of students using facial recognition technology. The main goal of this project is to make the attendance tracking and management system more efficient, time saving, simple and direct. Here, facial recognition technology is used to identify faces. The processed images are compared with existing saved datasets and attendance is recorded in the database accordingly.

Author(s) Details:

K. Pragathi,
BVRIT Hyderabad Collegeof Engineering for Women, India.

A. Spandana,
BVRIT Hyderabad Collegeof Engineering for Women, India.

Ch. Santosh Kumar,
BVRIT Hyderabad Collegeof Engineering for Women, India.

Please see the link here: https://stm.bookpi.org/COSTR-V6/article/view/8419

Recent Study on IoT Based Embedded Smart Lock Control System | Chapter 5 | New Insights into Physical Science Vol. 13

 Smart home protection and remote surveillance have become essential in recent years, and the need for smarter security systems has only grown with the introduction of new concepts such as the Internet of Things and the advancement of advanced authentication and security technologies. Security and surveillance have become increasingly important in recent years in a variety of settings, including homes, workplaces, banks, and other financial institutions. The development of an intelligent web-based door lock control system that uses face recognition technology for authentication, visitor tracking, and control. In this article, a smart door lock is discussed. Face detection is done with Haar-like features, and face recognition is done with the Local Binary Pattern Histogram (LBPH). The device also includes a web-based remote monitoring module, an authentication module, and a bare-bones embedded IoT server, which sends live pictures of visitors to the owner via email and SMS notification, and the owner can then remotely access the lock by responding to the email with predefined security codes to unlock it. the other entrance This device is useful in smart homes where the owner cannot be present at all times and remote authentication and control are required. The device was built and tested on a Raspberry Pi 2 board, and the various face recognition and control modules were programmed in Python and OpenCV. This paper also looks at the enormous potential of computer vision in general and face recognition in particular, as well as the possibilities of the Internet of Things in home security and automation. The OpenCV library, the Python programming language, and the Linux operating system's simplicity and prowess have all been thoroughly examined.

Author (s) Details

Hasan Bakheet Jasim
Department of Computer Science & Engineering, Imam Kadhim College for Isalmic Science University, Iraq.

View Book :- https://stm.bookpi.org/NIPS-V13/issue/view/51