An Overview Review of Artificial Intelligence with Real-Time Face Detection Application (original) (raw)
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Advanced Face Detection using Machine Learning And AI-based Algorithm
2022 5th International Conference on Contemporary Computing and Informatics (IC3I), 2022
In this study, the analysis of the research that has been provided in the "Advanced Face Detection using Machine Learning And AI-based algorithms". It is a facial acknowledgment framework that utilizes AI and Machine learning. It specifically uses the Support Vector Machine (SVM) system. Modern-day technology is making people amazed with the innovations that it is providing to make an individual's life simpler and easier. In the case of Face Recognition, it has proven to be the least intrusive and also the fastest form of the biometric verification system. Among all the tasks that can be done with the help of machine learning algorithms, one of the most crucial computer vision tasks is the use of face detection and recognition. The face detection and the recognition systems are both related to each other yet they are also very different from one another as well. By using machine learning the face detection aspect is used and it also is a widespread aspect of the face recognition system. Machine learning applies various algorithms for the detection of faces and the recognition process as well. Face detection and recognition systems are used in medical diagnosis and also in the deep analysis of human faces in the video for various intelligence purposes. Face recognition is a sub-category system of the biometric software system, it is used to map an individual's facial features and also to store that data as a face print for future purposes.
Real Time Face Recognition System Using Artificial Intelligence
2021
Face recognition is rapidly evolving, constantly developing, difficult and energizing spot applications. Over the last few years, an enormous measure of facial recognition calculations has been developed. For this case. The paper made an attempt to survey a variety of methods used to understand facial recognition. These include PCA, LDA, ICA, SVM, Gabor delicate wavelet, a PC device such as ANN for visibility and diversity. A crossbreed combination of these techniques. This update examines each of these approaches through boundaries facing recognition difficulties, such as lighting, standing distinction, outward appearances Keywords, part, formatting, style, styling, insert (key words)
The Real Time Face Detection and Recognition System
In the fast moving modern world everything is changed to provide a better life for humanity. New developments make this as a reality. So we were decided to develop the Real Time Face detection and recognition system. The importance of the automatic face detection and tracking system has increased as it is needed for video surveillance and new user interfaces. And providing higher security to the country. This system suitable for all people, but some people’s face damaged on acid attack, war and etc. so we were used to password for these peoples. In this research our effort is to develop a system for face detection and recognition in the real time. That will be effiient and give solution for many problems. The system was developed using C# .Net programming, Viola-Jones algorithm (Haar Cascade Classifir), PCA (classifid as either Feature based and image based) and EmguCV (Computer vision Library and wrapper class of Open CV)
Indian Journal, 2020
Background/objectives: The major changes which come across face recognition are to find the age and gender of the person. This study is centered on face detection with voice and biometric technology. Methodology: During this study, it has been worked on the input camera which takes multiple shots of person. After that, the Cascade Classification algorithm has been used inside the application which creates the multiple human templates. So the facial features have been detected. After that, it saved in particular database with their unique ID. Furthermore, the verification process has been started by matching the templates inside the database. Through this process, the student attendance has been marked automatically. Findings: It has been presented that face detection with voice and biometric technology can enhance the security measures. Employee's attendance can be marked by simply detecting face that can increase punctuality. Students can be checked and marked by face detection process. As well, it can also be setup in banks to enhance the security by allowing authorized people only or who have accounts in that bank. Novelty/improvements: For finding the age and gender from a particular image, relevant techniques are discussed, with some new approaches for maintaining security. We discussed complete models with security measures in this research.
Real Time Facial Recognition System Over Machine Learning
2020
The proposed work is inventedFace (facial) recognition is the identification of humans based real time surveillanceframework. Implementationis resolved existing security problem. In existing work, different security and surveillance mechanism used. These techniques are insufficient to give noble solution over critical surveillance needs. So there was need of such technique which gives strong solution over physical level security by using face identification as well as age , gender identification. All these categories will definitely increase security factors make our system more reliable. In advance we had considered age, gender and emotional characteristics in real time facial expressions. Our approach is fully depends on machine learning and python libraries which great deals with accuracy problem in real time applications. The big challenge will come while working with real time face images for correctly estimating facial features. Keywords-Open Computer Vision, Machine Learning,...
The impact of Artificial intelligence on surveillance camera system “Facial recognition growth”
2020
The fields of application of machine learning algorithms have no limits thanks to the focus of scientific research and its great impact on comfort in our daily life. Basically, with Artificial Intelligence (AI), the computer has become able to do tasks that were impossible in the past. The image processing strength and performance of the computer combined with Artificial Intelligence has contributed to the emergence of a new intelligence called facial recognition which contributed to the development of surveillance camera technologies and made them more effective.If, for example, When a company installs a surveillance camera without linking it to high-tech, its performance will still be limited in case the lighting level decreases , Blackout , Gradual change of lighting. Sudden change of lighting, shadows falsely detected as objects , and so on . This is the aspect that our paper deals with: the importance of Developing intelligent software that allows a camera to learn the faces of...
Design of Intelligent Facial Recognition System using AI for Surveillance Application
International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2020
Organizations presently continue to encounter significant security concerns; consequently, they require much particularly trained staff to achieve the coveted protection. This staff performs blunders that may affect the extent of security. A suggested solution to the matter mentioned above is a Face Recognition Security System, which can monitor and identify trespassers to blocked or high-security areas and assist in overcoming the margin of manual human oversight. This system is comprised of two halves: the hardware part and the software part. The hardware module incorporates a camera, while the software module includes software that uses face-detection and face-recognition algorithms. If a person infiltrates the confine in question, a set of snaps are captured by the camera and dispatched to the software to be examined/identified and equated with an existent database of trusted people. An alert is conveyed to the user if the infiltrator is not recognized.
Machine Learning Approach for Facial Image Detection System
Iraqi Journal of Science
Face detection systems are based on the assumption that each individual has a unique face structure and that computerized face matching is possible using facial symmetry. Face recognition technology has been employed for security purposes in many organizations and businesses throughout the world. This research examines the classifications in machine learning approaches using feature extraction for the facial image detection system. Due to its high level of accuracy and speed, the Viola-Jones method is utilized for facial detection using the MUCT database. The LDA feature extraction method is applied as an input to three algorithms of machine learning approaches, which are the J48, OneR, and JRip classifiers. The experiment’s result indicates that the J48 classifier with LDA achieves the highest performance with 96.0001% accuracy.
A real-time face detection and recognition system
2012 2nd International Conference on Consumer Electronics, Communications and Networks (CECNet), 2012
In the fast moving modern world everything is changed to provide a better life for humanity. New developments make this as a reality. So we were decided to develop the Real Time Face detection and recognition system. The importance of the automatic face detection and tracking system has increased as it is needed for video surveillance and new user interfaces. And providing higher security to the country. This system suitable for all people, but some people's face damaged on acid attack, war and etc. so we were used to password for these peoples. In this research our effort is to develop a system for face detection and recognition in the real time. That will be efficient and give solution for many problems. The system was developed using C# .Net programming, Viola-Jones algorithm (Haar Cascade Classifier), PCA (classified as either Feature based and image based) and EmguCV (Computer vision Library and wrapper class of Open CV).
Advancing in variable scopes in network technology, many new technologies were developed. Security issues were important, especially in the detection and recognition of people using variable methods like face details. Sensors have been used widely in recent days to support security systems. Sensors are devices used to convert any type of signals into electrical signals that are recorded to be processed later. These signals can be viewed by the user in several ways. Sensors increased in the development stage that can be integrated with operating systems, data storage systems, processing units, communication units, and any other function units. Detection and recognition systems were developed into a new level of technology. Some systems like figure print and palm lines face many problems because the possible change of the skin structure can be faced in time. So, these methods faced a certain problem and limitations that caused them to search for other methods more accurate. This search aims to create a new method for face detection and recognition depending on sensors. Most of the methods used for face recognition depend on OpenCV libraries that give good accuracy and time recovery availability. On the other hand, practical applications were developed to increase the accuracy of these systems like SeetaFace and YouTu methods. Three methods of detection were important to be detected too to increase the accuracy of the whole system which are the side face detection, the occlusion detection, and the face expressions. Then these data were compared to create the whole accuracy result of the system.