Home Security Using Facial Recognition System

Call for Papers: Special Issue ofComputer Vision and Image Understandingon the Analysis of Volumetric Images

Laterality effects in normal subjects' recognition of familiar faces, voices and names. Perceptual and representational components

Abstract— In this digital era, face recognition system plays a vital role in almost every sector. Face recognition is one of the mostly used biometrics. It can used for security, authentication, identification, and has got many more advantages. Despite of having low accuracy when compared to iris recognition and fingerprint recognition, it is being widely used due to its contactless and non-invasive process. Furthermore, face recognition system can also be used for attendance marking in schools, colleges, offices, etc. This system aims to build a class attendance system which uses the concept of face recognition as existing manual attendance system is time consuming and cumbersome to maintain. And there may be chances of proxy attendance. Thus, the need for this system increases. This system consists of four phases- database creation, face detection, face recognition, attendance updation. Database is created by the images of the students in class. Face detection and recognition is performed using Haar-Cascade classifier and Local Binary Pattern Histogram algorithm respectively. Faces are detected and recognized from live streaming video of the classroom. Keywords—Face Recognition; Face Detection; Haar-Cascade; Opencv :library are used in face recognition system.

Face recognition is an area of computer vision that has attracted a lot of interest from the research community. A growing demand for robust face recognition software in security applications has driven the development of interesting approaches in this field. A large quantity of research in face recognition deals with visible face images. In the visible spectrum the illumination and face expressions changes represent a significant challenge for the recognition system. To avoid these problems, researchers proposed recently the use of 3D and infrared imaging for face recognition. In this work, we introduce a new framework for infrared face recognition using texture descriptors. This framework exploits linear and non linear dimensionality reduction techniques for face learning and recognition in the texture space. Active and passive infrared imaging modalities are used and comparison with visible face recognition is performed. Two multispectral face recognition databases were used in our experiments: Equinox Database (Visible, SWIR, MWIR, LWIR) and Laval University Multispectral Database (Visible, NIR, MWIR, LWIR). The obtained results show high increase in recognition performance when texture descriptors like LBP (Local Binary Pattern) and LTP (Local Ternary Pattern) are used. The best result was obtained in the short wave infrared spectrum (SWIR) using non linear dimensionality reduction techniques.

Face recognition on mobile phones is gaining increasing attention due to the great variety of applications it can offer. In this paper, a fast recognition system on mobile phone is presented. First, Haar-like features with AdaBoost is used to detect face in pictures taken from phone camera. Second, in order to detect the eyes of face, we use a fast and effective method - eyes template matching in specific regions of the face, and then the local binary pattern (LBP) is adopted for fast face recognition. The experimental results demonstrate that the system introduced has a good performance in terms of recognition time and rate. Besides, various optimization techniques to speed up the face detection and recognition process are discussed.

There is an abnormal increase in the crime rate and also the number of criminals are increasing, this leads towards a great concern about the security issues. Crime preventions and criminal identification are the primary issues before the police personnel, since property and lives protection are the basic concerns of the police but to combat the crime, the availability of police personnel is limited. With the advent of security technology, cameras especially CCTV have been installed in many public and private areas to provide surveillance activities. The footage of the CCTV can be used to identify suspects on scene. This Real time criminal identification system based on face recognition works with a fully automated facial recognition system. Haar feature-based cascade classifier and OpenCV LBPH (Local Binary Pattern Histograms) Algorithms are used for Face detection and recognition. This system will be able to detect face and recognize face automatically in real time. An accurate location of the face is still a challenging task. This Framework has been widely used by researchers in order to detect the location of faces and objects in a given image. Face detection classifiers are shared by public communities, such as OpenCV. The automatically tagging feature adds a new dimension to sharing pictures among the people who are in the picture and also gives the idea to other people about who the person is in the image. In our project, we have studied and implemented a pretty simple but very effective face detection using CNN algorithm which takes human skin color into account. Our aim, which we believe we have reached, was to develop a system that can be used by police or investigation department to recognize criminal from their faces. The method of face recognition used is fast, robust, reasonably simple and accurate with a relatively simple and easy to understand algorithms and technique. Key Words: Face Detection, MTCNN, Deep Learning, Real-Time Surveillance, Criminal Identification, CNN

Facial recognition is a feature that uses facial detection algorithms to detect a face and then invokes facial recognition algorithms to try to match the person’s face. First, a person’s face needs to be enrolled; during enrollment, the person’s facial details are saved in a database—in this case, a mobile phone. The facial recognition algorithm uses this database to match the currently presented face with the faces saved in the database. The efficiency of a facial recognition algorithm depends upon the speed at which it can detect and recognize faces. The problem we address in this paper is that a reliable, automated method for testing facial recognition features in mobile phones. Because the multimedia capabilities of smartphones have expanded phenomenally, the need for thoroughly testing facial recognition algorithms has become crucial. The uses of facial recognition can range from facial authentication for unlocking phones to security for a number of other applications. To meet these needs, a reliable, automated test for validating the facial recognition algorithms must be developed. The challenge for the software test team was to automate the test cases, which involve tilting the phone at specific angles from the test subject. The permissible angular movements of the phone are determined by the algorithmic specifications of the facial recognition algorithm. We tested scenarios involving multiple faces, motion blur, panning the phone in front of the test subject faces at various speeds, and so on. We adopted a robotic arm to perform the facial recognition test cases and developed software to program the robotic arm to test the phone’s facial recognition software for functionality, performance, and stability as well as adversarial tests. This paper discusses the computer vision use case for facial recognition and describes how we developed an automated facial recognition test suite using a robotic arm.

Video surveillance systems play a crucial role in ensuring public safety and security. However, the traditional methods of surveillance often fall short in effectively identifying individuals, particularly in crowded or dynamic environments. This research addresses the limitations of conventional video surveillance by proposing a machine learning-based facial recognition system. The increasing demand for robust security measures necessitates the development of advanced technologies in video surveillance. Facial recognition has emerged as a promising solution, but existing systems struggle with accuracy and efficiency. This research aims to bridge these gaps by leveraging machine learning techniques for facial recognition in video surveillance. Conventional video surveillance struggles with accurate and rapid identification of individuals, leading to potential security lapses. This research addresses the challenge of enhancing facial recognition accuracy in real-time video feeds, especially in scenarios with varying lighting conditions and occlusions. While facial recognition has gained traction, there is a significant research gap in the implementation of machine learning algorithms tailored for video surveillance. This study aims to fill this void by proposing a novel methodology that combines deep learning and computer vision techniques for robust facial recognition in dynamic environments. The proposed methodology involves training a deep neural network on a diverse dataset of facial images to enable the model to learn intricate facial features. Additionally, computer vision algorithms will be employed to handle challenges such as occlusions and varying lighting conditions. The model''''s performance will be evaluated using real-world video surveillance data. Preliminary results demonstrate a significant improvement in facial recognition accuracy compared to traditional methods. The machine learning-based system exhibits enhanced performance in challenging scenarios, showcasing its potential for practical implementation in video surveillance systems.

In recent days, our world has entered the age of advanced digital systems, which means humans has to interact with technology in all scopes of live, this leads too as a result to another main concept, security. There are many security technologies that was developed to keep user information save. for example, there are several technologies used to let just the accurate user to log into devices like fingerprint, face recognition, or any another type of these systems. These technologies are in continuous development and are still suffering from accuracy, so several studies were performed to get the best accuracy for any of these systems. Computer vision was developed to give the devices the ability to see and recognize objects in an image or a video scene. It is a science that aims to build an intelligent application that are able to understand the content inside images as humans can do. To create a system like this, first step is to make Data acquisition, this task is done by cameras which gives us a sequence of frames when we record a video. After that, this data is analyzed and studied to extract important features from it. This feature will allow us to re- construct the description of the outer world in a manner that can understood by computer system. Face recognition systems are used on a wide range in mobile applications, banks, and military sites, police stations to make a recognition of the criminals, even in covid19 situations the face recognition was used too. Our proposed method aims to increase the accuracy a face recognition system using deep learning models, the system proposed is able to work on any platform compiled with python which is the application was used in our study. The model performed gives a high accuracy with a very speed result.

Application of Face Recognition Method Under Deep Learning Algorithm in Embedded Systems

When it comes to recognizing someone, the most significant feature is their face. Face recognition aids in verifying any person's identification by using his particular traits because it acts as an individual identity for everyone. The whole technique for authenticating any face data is separated into two stages. Face Recognition system is used in the first step. is done rapidly unless in circumstances when the item is put relatively far away, and then the second phase begins in which the face is identified as a person. The entire process is then repeated, assisting in the development of a face recognition model, which is regarded to be one of the most meticulously planned biometric technologies. The photographs of people's faces are collected by people, and also the images are processed immediately by the identification equipment. As a result, the paper offers pertinent facial recognition research from a variety of perspectives. The study outlines the developmental stages and the technology associated with facial recognition. We offer face detection and recognition analysis research for real-world settings, as well as universal Face recognition databases and assessment criteria We take a look at face recognition in advance. Face recognition has emerged as a viable future growth path with a variety of applications.

Face recognition is among the most productive image processing applications and has a pivotal role in the technical field. In the recent time automated face recognition has become a trend and has been developed very much, this is mainly due to two reasons first it is due to availability of modern technologies and second is due to the ability to save time using face recognition in the process of taking attendance of students. Its usage will grow vast in the future as it saves a lot of time. It consumes a lot of time to take attendance manually and few might also fake the attendance, in order to prevent time consumption and avoid faking the attendance. Face recognition is used to identify the person present in the class and mark their attendance , this is done with the help of image or video frame. We proposed an automatic attendance management system using techniques such as PCA algorithm. The face detection and recognition will automatically detect the students in the classroom and mark the attendance by recognizing the person. The traditional process of making attendance and present biometric systems are vulnerable to proxies. This paper is therefore proposed to tackle all these problems. The proposed system makes the use of Haar classifiers, KNN, CNN, SVM, Generative adversarial networks, and Gabor filters. After face recognition attendance reports will be generated and stored in excel format. The system is tested under various conditions like illumination, head movements, the variation of distance between the student and cameras. After vigorous testing overall complexity and accuracy are calculated. The Proposed system proved to be an efficient and robust device for taking attendance in a classroom without any time consumption and manual work. The system developed is cost-efficient and need less installation

In the current scenario face identification and recognition is an important technique in surveillance. The face is a necessary biometric in humans. Therefore face detection plays a major job in computer vision applications. Several face recognition and emotions classification approaches have been presented throughout the last few decades of research to improve the rate of face recognition for thermal pictures. However, in real-time, lighting conditions might change due to several factors, such as the different times of capture, weather, etc. Due to variations in lighting intensity, the performance of the facial expression recognition system is not good. This paper proposed a model for human thermal face detection and expression classification. Four main steps were involved in this research. Initially, the Difference of the Gaussian (DOG) filter is utilized to crop the input thermal images and then normalize the images using the median filter in pre-processing step. Then, Efficient Net is used for extracting features such as shape, location, and occurrences from thermal face images. After that, detect human faces utilized by the YOLOv4 technique to better emotions classification. Finally, classify the emotions on faces by using the DenseNet technique into seven emotions such as happy, sad, disgust, surprise, anger, fear, and neutral. The proposed method outperforms state-of-art techniques for face recognition on thermal pictures, and classifies the expressions, according to experimentations on the RGB-D-T database. The accuracy, precision, recall, and f1-score metrics will be utilized with the database to assess the efficacy of the proposed methodology. The proposed models achieve a high classification accuracy of 95.97% on the RGB-D-T database. Furthermore, the outcomes show good precision for various face recognition tasks.

Face recognition system has always been a crucial human-computer interaction tool in most security systems, access control, video surveillance, commercial areas, and networks like Facebook as well. After development in the area of artificial intelligence and growing security concerns, face recognition system has attracted attention by providing maximum efficiency in face detection and recognition. Face recognition is considered to be a biometric artificial intelligence-based application. Face recognition is the process of recognizing the face of a person by a vision system. As an extension to the existing face recognition system in this model, a new feature is added i.e., a voice message as output that is produced for each face recognized. The voice message could be anything like a simple hello or even a complex sentence. Keywords: Artificial intelligence, Face recognition, voice message, OpenCV, pyttsx.

A typical issue in biometry analysis could be face recognition from an images or videos. Since face recognition doesn’t require the cooperation of the object, it plays a crucial role in closed circuit television. The advantages of face-based identification over other biometry are individualism and acceptance as a face can be a dynamic object. Many public spaces regularly have police investigation cameras for video capture. These cameras have their important worth for security. This paper’s objective is to judge the various face detection and recognition techniques and provide a comprehensive solution for image based face detection and recognition with high accuracy and a higher response rate as a first step in video police investigation. The solution is projected supported by tests that were carried out on various face to rich databases in terms of subjects’ ability to evoke emotions and their weight and race.