Real Time Facial Recognition System Over Machine Learning

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)

— in this paper We present a system for real-time, saved images And Video Recorded in this three Method We Can detecting and recognizing faces of a criminal at public place Or Checkpoint such that the system easy to learn And to implementation ,the three method that used in the system make recognition very accuracy , where if we running it will appear Login window in this screen should insert (username And Password) to enter in the main window, in the main window we must firstly insert images to Database and Then choose the way that want to recognition, if the system recognition the Face, the system will show the name of the criminal and in the same time the system will appear Danger Sound Refer that the Criminals Recognition and Matched in the Database , Besides that we can make Easy update(Rename ,Delete) to the Database without complexed. 1. Introduction Face recognition is one of the most active and widely used technique [1][2] because of its reliability and accuracy in the process of recognizing and verifying a person's identity. The need is becoming important since people are getting aware of security and privacy. For the Researchers Face Recognition is among the tedious work. It is all because the human face is very robust in nature; in fact, a person's face can change very much during short periods of time (from one day to another) and because of long periods of time (a difference of months or years). One problem of face recognition is the fact that different faces could seem very similar; therefore, a discrimination task is needed. On the other hand, when we analyze the same face, many characteristics may have changed. These changes might be because of changes in the different parameters. The parameters are: illumination, variability in facial expressions, the presence of accessories (glasses, beards, etc.); poses, age, finally background. We can divide face recognition [3][4] techniques into two big groups, the applications that required face identification and the ones that need face verification. The difference is that the first one uses a face to match with other one on a database; on the other hand, the verification technique tries to verify a human face from a given sample of that face. Face recognition has been an active research area over the last 30 years. It has been studied by scientists from different areas of psychophysical sciences and those from different areas of computer sciences. Psychologists and neuroscientists mainly deal with the human perception part of the topic, whereas engineers studying on machine recognition of human faces deal with the computational aspects of face recognition. Face recognition has applications mainly in the fields of biometrics, access control, law enforcement, and security and surveillance systems. 2. Biometric Measures Biometric systems are automated methods for identifying people through physiological or behavioral characteristics. Face recognition As compared with other biometrics systems using fingerprint/palm-print and iris, face recognition has distinct advantages because of its noncontact process. Face images can be(system would allow a person to be identified by walking in front of a camera and captured from a distance without touching the person being identified, and the identification does not require interacting with the person. In addition, face recognition serves the crime deterrent purpose because face images that have been recorded and archived can later help identify a person .

The demand of smart cameras for various applications in surveillance for security purpose is growing rapidly. However till now, the handling required for real time face detection prohibits integration of the complete application into a small sized camera. The Real Time Face Tracking and Identification for Surveillance System proposed in this research work has originated ahead with a simple and an efficient approach to satisfy the handling requirements and enable constant and precise face detection. The core functionality of Real Time Face Tracking and Identification for Surveillance System is to allow tracking and recognition of human faces in a video stream and thereby provide a centralized, cost effective and robust mechanism of securing business and government premises. The primary objective of the proposed system is to reduce detection redundancy, minimize misclassification risk, provide huge processing capacity and work within a reasonable computational budget.

International Journal of Advanced Research in Computer Science, 2020

Authentication is a major challenge in computer-based communication system operation. Human face recognition is an important branch of biometric verification and has been commonly used in many applications, including video monitoring system, human-computer interaction, door control system, and network protection. A computational neural network can be implemented as a framework in the face classification process. In order to recognize dimensions within the intend-able limits, the test system offers acceptable performance. The device also allows multiple faces to be identified and recognized in live pictures. Using OpenCV and programming with Python, we design a real-time face recognition system.

2020

The biometric is an analysis of the human behavior and characteristics. Face-recognition is biometric technique in which Different approaches are used for this. In this article, a survey is included for all these strategies to evaluate different algorithms and processes. Face recognition is the new biometric division for health, since no faces can be overcome as a security strategy. So, how can we recognize a face using face detection method with the help of computer is given in this paper.

International journal of engineering research and technology, 2019

In the coming world of digitalization the human life is going to be much faster than it is now. And the most important part of the human life is going to be the security. In our day to day life we are using some usual methods for the security purpose some of them are passwords, biometric scan, one time password (OTP) and retinal scan. But as the technology grows and techniques are going to be invented these thing are not going to be the most secure methods to keep secure the data. For that purpose we have to step forward and apply new method for keeping surveillance and security i.e. the facial recognition techniques which is the most secure and reliable all out of above. In this paper we are going to give the whole survey of the security method that is Facial Recognition and the current stage of it. The use of this technique can make the surveillance much better than it is it has vast application areas we can apply this technique in most of the security systems.

Security is generally a state or feeling of being saved and protected, an assurance that something of value will not be taken. This paper employs two of the emerging artificial intelligence technologies: Facial Recognition and Artificial Neural Networks for developing a secure keyless door where authentication of authorized faces are the only guarantee for entry. This mechanically built door, with a camera, has an interface with the PC for capturing and processing images.

Today's institutions are facing major security issues; consequently, they need several specially trained personnel to attain the desired security. These personnel, as human beings, make mistakes that might affect the level of security. A proposed solution to the aforementioned matter is a Face Recognition Security System, which can detect intruders to restricted or high-security areas, and help in minimizing human error. This system is composed of two parts: hardware part and software part. The hardware part consists of a camera, while the software part consists of face-detection and face-recognition algorithms software. When a person enters to the zone in question, a series of snapshots are taken by the camera and sent to the software to be analyzed and compared with an existing database of trusted people. An alarm goes off if the user is not recognized. This is a project to develop an e-mail application using HCI features helping users check their mailbox easier. It is consist of a network module to develop a standalone email application to ask user home security. a face recognition module Face recognition algorithm and pick the fastest one to avoid lag and use it as a security level of application. This paper describes the technique for real time human face detection and tracking using a modified version of the algorithm suggested by Paul viola and Michael Jones. The paper starts with the introduction to human face detection and tracking, followed by apprehension of the Vila Jones algorithm and then discussing about the implementation in real video applications. Viola Jones algorithm was based on object detection by extracting some specific features from the image. We used the same approach for real time human face detection and tracking. Simulation results of this developed algorithm shows the Real time human face detection and tracking supporting up to 50 human faces. This algorithm computes data and produce results in just a mere fraction of seconds

IRJET, 2020

The main objective of this project to develop a real time facial analysis using opencv and tensorflow for artisum people where user can see the facial expression, age and gender of the male and female. Face regonition and analysis from an image and video. Most signal or image processing algorithms should be designed with real time execution in mind.Facial features are considered as one of the important personal characteristics. This can be used in many applications, such as face recognition and age estimation. The value of these applications depends in several areas, such as security applications, law enforcement applications, and attendance systems. In addition, facial features are particularly the key usage in the finding of lost child. Present applications have achieved a high level of accuracy. This paper provides a survey of face recognition, including the age estimation, which was discussed. Moreover, the research outlines several challenges faced in face recognition area that had been explored. The research also provides a landscape mapping based on integrating into a critical and coherent taxonomy. In the methodology sections, the exploration the accomplished via a deep focused in every single article in ''Face Recognition'', then ''Age Estimation'', and later in ''Facial Features''. The ''Articles extraction'' is mining from diverse sources, such as Web of Science, ACM, IEEE, Science Direct, and Springer databases. The research covers overall 72 articles; 32/72 articles were face recognition. Moreover, 39/72 of the articles were for age estimation. A comparison based on the objectives of the approaches is presented to underline the taxonomy. Ending by research conclusion on face techniques contributes to the understanding of the recognition approaches, which can be used in future researches. The research concluded that face techniques' performance is distinct from one data set to another. This paper contributes to display gaps for other researchers to join this line of research.

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)