There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Survey of intelligent surveillance system for monitoring international border security

In this paper we will highlighting of Fuzzy logic that used to detect emotion subjects from textual data. Fuzzy Logic was founded to handling non-value member and convert it to a value member. In this research we will review the history of fuzzy logic with the emotions detection and the techniques used. Emotions are basic part of human internal activity and execute a fateful role of making a decisions and cognitive relation processes such as (sadness, surprise, happiness, disgust, anger, and fear). Emotions can be expressed through deferent forms such as speech, facial expressions, gestures and text. Now the research is working in the field of emotion detection and sentiment analysis. The researchers are working to develop a system to classify emotions represented in text.

Fuzzy Logic used in Textual Emotion Detection

Nowadays, the verification of handwritten signatures has become an effective research field in computer vision as well as machine learning. Signature verification is naturally formulated as a machine-learning task. This task is performed by determining if the signature is genuine or forged. Therefore, it is considered a two‐class classification issue. Since handwritten signatures are widely used in legal documents and financial transactions, it is important for researchers to select an efficient machine-learning technique for verifying these signatures and to avoid forgeries that may cause many losses to customers. So far, great outcomes have been obtained when using machine learning techniques in terms of equal error rates and calculations. This paper presents a comprehensive review of the latest studies and results in the last 10 years in the field of online and offline handwritten signature verification. More than 20 research papers were used to make a comparison between datasets, feature extraction, and classification techniques used in each system, taking into consideration the problems that occur in each. In addition, the general limitations and advantages of machine-learning techniques that are used to classify or extract signature features were summarized in the form of a table. We also present the general steps of the verification system and a list of the most considerable datasets available in online and offline fields.

https://downloads.hindawi.com/journals/sp/2022/8170424.pdf

A Comparative Study among Handwritten Signature Verification Methods Using Machine Learning Techniques

This paper presents a system which configures a web- based access monitoring system for banks. The banking system plays a very significant role in society. There is a need for monitoring the people entering the bank as a part of the procedure for providing security. The traditional monitoring system only records the video, it does not recognize the people entering. This work implements a system for recognizing the people entering the bank using a computer vision technology. With the ongoing development of image processing algorithms, the computer vision discipline of artificial intelligence launching a new era by teaching computers to comprehend and interpret the visual environment. This project proposes an efficient way to keep track of the people entering the banks using face detection. Feature extraction of the images of the employees and the customers is done. The database of the face images of employees of the bank and customers is created and stored in the system. Viola jones algorithm which uses HAAR feature extraction and cascade classifiers is used for the face detection. The face features detected is compared with the existing database and classified as employee, customer and unknown. After the face recognition the information is displayed in a webpage with date and time of entry. This webpage can be very useful in case of Security breach in banks. The face detection algorithm is implemented using OpenCV on a Raspberry pi processor with Linux operating system.

Design and implementation of access monitoring system for banks using OpenCV and Django

Face recognition can be considered as a significant section for the aim of securing and monitoring, specifically for disabled people via utilizing the algorithm of real-time face recognition. A Raspberry Pi-based face recognition system using traditional mechanisms of face detection and recognition will be provided so the way that image-based biometrics utilizes a Raspberry Pi is depicted. This paper goal is moving face recognition towards a level where the system can substitute the using of a password and RFI-Cards for accessing security systems. The outcome of the submitted suggestion can be a working system utilizing recognition of face using OpenCV, Raspberry Pi, and on an Android app on cell phones. The document establishes a basis utilizing those three elements on systems can be applied in several extra scenarios of recognition of face in combining on cheap machinery, and effective system according to its cost.

A Raspberry PI Real-Time Identification System on Face Recognition

Smart home indicates an application for different technological implementations, it could indicate any system which controls the door lock and several other devices. Facial identification which is an important section to achieve surveillance and safety, especially for handicapped people, can be considered as one of the ways that deal with biometrics and performed to identify facial images via utilizing fundamental features of the face. A Raspberry Pi-based face recognition system using conventional face detection and recognition techniques is going to be supplied, so the method in which image-built biometrics uses a Raspberry Pi is described. The aim of the paper here can be considered as transferring face recognition to a level in which the system can replace the utilizing of RF I-Cards and a password to access any system of security and making the system alive and protect the door from being open by hackers, especially by using the picture of an authorized person, we make the raspberry pi turn off and cannot turn on only by a command from the authorized person's mobile. The result of the presented proposal is a system that uses face recognition by utilizing OpenCV, Raspberry Pi, and it functions on an application of Android, and this system percentage becomes 99.63%. It should be cost-effective, of high performance, secured, and easy to use, which can be used in any smart home application.

/pdf/smart-door-for-handicapped-people-via-face-recognition-and-1pij7bv1pq.pdf

Rana Talib Rasheed

Papers

A Raspberry PI Real-Time Identification System on Face Recognition

Smart Door for Handicapped People via Face Recognition and Voice Command Technique