Cloud Data Access Prevention Method in Face Recognition Technology Based on Computer Vision

With the deepening development of communication technology, the technology of automatic modulation and recognition of communication signals has been more and more widely used in military and civilian fields. This paper mainly studies the implementation of automatic modulation recognition using deep learning as a computing tool, focusing on CNN neural network and LSTM neural network, and conducting simulation experiments on public data sets. Based on the original CNN neural network, this paper introduces the structure of LSTM neural network and combines the advantages of the two types of neural networks to explore a combined neural network that is superior to the originally used CNN network. The experimental results of this thesis show that introducing the features of dynamic time series modeling of LSTM networks into deep learning networks can capture the global and local information of signals more effectively and improve the accuracy of neural networks in automatic modulation recognition.

The paper presents a research and development of a face template creation sub-system, which serves a part of a face-based identification system. This system is intended to be used in surveillance camera systems. The paper addresses a problem of contradicting requirements on the face recognition system - near-real time recognition and a low error rate. The research, we have conducted, has shown that a solution may be found in designing an adequate face template database and in applying appropriate face template creation algorithms. Face templates have a significant influence on the correct classification rate, while the number of temples is directly proportional to the classification time. The results of our research have so far proved that using the Centroid Method for face templates creation is the best performing approach. The paper presents two novel methods for a face template creation in order to investigate whether the Centroid Method may be outperformed. The performance of proposed face template methods is evaluated on images from an extensive IFaViD database assembled during our research, images were captured by a surveillance camera system.

Face recognition is widely used as identity authentication method in the Internet of Things (IoTs). However, compared with static camera, the background of IoT devices may change. Additionally, the facial expression or posture may change as well. Although face recognition has been researched for several decades, it is still a challenging task to design a robust face recognition system in IoT environment. This paper designs a special face recognition system by introducing deep transfer learning to adapt difference scenes in IoT environment. The face recognition system consists of two parts: terminal smart devices and cloud center. The terminal smart device consists of a camera to capture face picture, a wireless network part to communicate with cloud center, and a screen to show recognition result. The cloud center receives the face picture from terminal smart device and returns the face recognition result by using an offline deep transfer learning model. Compared with previous models, deep transfer learning model can better adapt different scenes. The experiments shows that the proposed face recognition system achieves more promising results than two source‐only models and two shallow transfer learning models.

Deeply-learned face representations enable the success of current face recognition systems. Despite the ability of these representations to encode the identity of an individual, recent works have shown that more information is stored within, such as demographics, image characteristics, and social traits. This threatens the user's privacy, since for many applications these templates are expected to be solely used for recognition purposes. Knowing the encoded information in face templates helps to develop bias-mitigating and privacy-preserving face recognition technologies. This work aims to support the development of these two branches by analysing face templates regarding 113 attributes. Experiments were conducted on two publicly available face embeddings. For evaluating the predictability of the attributes, we trained a massive attribute classifier that is additionally able to accurately state its prediction confidence. This allows us to make more sophisticated statements about the attribute predictability. The results demonstrate that up to 74 attributes can be accurately predicted from face templates. Especially non-permanent attributes, such as age, hairstyles, haircolors, beards, and various accessories, found to be easily-predictable. Since face recognition systems aim to be robust against these variations, future research might build on this work to develop more understandable privacy preserving solutions and build robust and fair face templates.

Face recognition is one of the fastest-growing technology in the field of biometrics, electronic business (e-business), and image processing. With the increase of e-business, the lack of transaction security has led to the increase in fraudulent activities. Most systems such as e-business transactions require a reliable recognition system to provide services to authentic and legitimate users. Biometric recognition using facial traits is the solution to dealing with the security issues in e-business transactions. Biometric recognition is the process of digitally recognizing a person based on their physiological or behavioral traits. Face recognition technology is applied in the fields like e-business, home security, and criminal identification. This paper proposes the implementation of the face recognition system for login in e-business transactions. A three-phase enhanced e-business security system that consists of face detection, feature extraction, and face recognition is designed to ensure that the security in e-business transactions is always maintained. The biometric face recognition system will reduce the transaction security issues in e-business. The python OpenCV library was used to simulate and evaluate the effectiveness of the face recognition system. Experimental results are provided to illustrate the effectiveness of the face recognition system.

Face Recognition (FR) is one of the most thriving fields of contemporary research, and despite its universal application in authentication and verification systems, ensuring its effectiveness in unconstrained scenarios has predominantly remained an on-going challenge in Computer Vision, because FR systems experience considerable loss in performance, when there exists significant variation between the test and database faces in terms of attributes such as Pose, Camera Angle, Illumination and so on. The potency of FR systems markedly declines in the presence of noise in a given face and furthermore, the performance is also determined to a large degree by the Feature Extraction technique that is employed. Hence in this paper, we propose a novel mechanism known as Fuzzy-GIST, that can proficiently perform FR by adeptly handling real-time images (which contain the aforementioned unconstrained attributes) in low-powered portable devices by employing Fuzzy Filters to eliminate extraneous noise in the facial image, prior to feature extraction using the computationally less demanding GIST descriptor. Backed by relevant mathematical defense, we will establish the efficacy of our proposed system by conducting detailed experimentations on the ORL and IIT-K databases.

Rapider responses are required from ATM banking suppliers to security requests. The current network environment has made it easier for hackers to engage in illicit activity. One of the most pressing issues confronting modern IT teams is network security. There is no doubt that ATM financial transactions should be protected against security risks by several different layers, but service providers should also incorporate security implications in their service offerings. And a lot concerning security flaws, credit card or identity card fraud, and methods for thieves and crackers to get their hands on any logical pin numbers or passwords. Identification, used in the current architecture to permit a system's access, is analogous to a username. Since passwords can be hijacked or misplaced, it is crucial to utilize authentication to confirm that the intended user is truly who their claim to be. The most recent solutions to security and privacy issues are biometric identification and authentication systems. Face recognition is the study of characteristics of a person being that can be used to recognize them. These physical characteristics include, among other things, fingerprints, faces, hand shapes, voices. This study surveys the various security system authentication methods. Top-level safety in authentication is provided by this facial biometric with QR code verification.

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.

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.

Security is very important issue in today’s time. Face recognition, or facial recognition, is one of the largest areas of research within computer vision. The development of facial recognition systems has also developed and has been implemented for home door locking systems and is an option that is quite simple and easy to use and is quite accurate in recognizing the face of homeowners. We can now use face recognition to unlock our mobile phones, verify identification at security gates, and in some countries, make purchases. Although lot of progress has been made in domain of face detection and recognition for security, identification and attendance purpose, but still there are issues hindering the progress to reach or surpass human level accuracy. This paper consists of face recognition and Machine Learning for detecting an intruder and recognition.KeywordsMachine learningSecurityFace recognitionImage processingPython

Face recognition innovation has become one of the most well-known research regions in PC vision since it can possibly be utilized for an assortment of business and government applications. PC calculations are utilized by face recognition frameworks to pick specific, perceived perspectives on an individual's face. There should be a mutual component that shows the innate characteristics of the face that are free of the image modalities since a similar individual's face pictures from different picture modalities are connected to a similar face object. In this paper, a Mutual Component Analysis (MCA) is proposed and different element extraction strategies are likewise tried to contrast execution all together with construe the mutual components for reliable heterogeneous face recognition. A face ID strategy in view of thick framework histograms of oriented gradients (HOG) is proposed to catch facial highlights in complex circumstances sufficiently. The HOG highlights are first taken from the face picture after it has been isolated into a few thick lattices. Then, at that point, the closest neighbor classifier is utilized for recognition in the wake of making completely out of the framework HOG highlight vectors to understand the component appearance of the whole face. As per the trial discoveries, the thick matrix HOG approach is more fit to represent changes in time and climate. The thick matrix HOG and LBP both take about a similar measure of time to remove highlights. The thick lattice HOG approach, instead of the LBP, accomplishes a higher recognition rate while utilizing less aspects.

Cortical neurons that are selectively sensitive to faces, parts of faces and particular facial expressions are concentrated in the banks and floor of the superior temporal sulcus in macaque monkeys. Their existence has prompted suggestions that it is damage to such a region in the human brain that leads to prosopagnosia: the inability to recognize faces or to discriminate between faces. This was tested by removing the face-cell area in a group of monkeys. The animals learned to discriminate between pictures of faces or inanimate objects, to select the odd face from a group, to inspect a face then select the matching face from a pair of faces after a variable delay, to discriminate between novel and familiar faces, and to identify specific faces. Removing the face-cell area produced no or little impairment which in the latter case was not specific for faces. In contrast, several prosopagnosic patients were impaired at several of these tasks. The animals were less able than before to discern the angle of regard in pictures of faces, suggesting that this area of the brain may be concerned with the perception of facial expression and bearing, which are important social signals in primates.

Face recognition technology, defined as the process of detecting and verifying individuals’ identities via image processing techniques in computer vision, has become increasingly vital in today’s technologically and digitally evolved world. Automating the attendance system with face recognition software eliminates inefficiencies like tampered records, proxies, and security risks. This paper proposes incorporating facial recognition technology, Haar Cascade classifiers (Li et al. in ICEMI 2017-Proceedings IEEE 13th International conference on electronic measurement and instruments, vol 2018-Janua, pp 483–487), and the Local Binary Pattern Histogram (LBPH) (Ahmed et al. in 2018 International conference artificial intelligent big data, (ICAIBD 2018), pp 144–147) machine learning approach can be used for identifying faces to create a secure and reliable solution for automating attendance systems. The proposed method produces more accurate results than any other algorithms used earlier. Compared to existing algorithms, the suggested system showed better results, reliability, and robustness in accurately recognizing faces. Its facial recognition accuracy is 87%, and the false-positive rate is only 7%. The system can also reliably identify a person who has undergone unintentional changes in their appearance, such as growing a beard or putting on spectacles.

This research aims to develop artificial intelligence (AI) algorithms in the context of facial recognition with a focus on increasing accuracy in difficult environmental conditions. Although facial recognition technology has made great progress, challenges such as poor lighting, variations in facial expressions, and head rotation are still problems that must be overcome. The research methodology involved collecting a wide dataset covering a wide variety of faces under various environmental conditions. This data is then processed and its features are extracted using computer image processing techniques. Furthermore, several deep neural network architectures, such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), were developed, trained, and evaluated for face recognition tasks. The expected result is the development of an AI algorithm that is able to overcome challenges in facial recognition with higher accuracy than existing methods. In particular, significant improvements in facial recognition accuracy are expected especially under low lighting conditions and variations in facial expressions. This research has a major impact in a variety of security applications, such as border surveillance, building access control, and corporate security. With higher facial recognition accuracy, security risks can be significantly reduced, resulting in safer and more efficient security solutions. In conclusion, this research aims to bring innovation in facial recognition technology through advanced AI approaches, with the potential to improve security in various contexts.

Face Recognition (FR) is a significant area in computer vision plus pattern recognition. The face is the easiest mode to discriminate the specific individuality of every other. FR is a particular identification scheme that usages particular features of an individual to recognize the individual's identity. The challenges in FR are aged, facial terms, variations in the imaging surroundings, illumination plus posture of the face. Specially, in this study firstly we mark an outline of FR that includes definition, types and problems. Secondly, we provided a complete related work of FR. The objective of this study is to provide a comprehensive outline on the work that has been carried out over the previous spans in the progressing area of FR. This study offers an extensive view of theories, methodologies, up-to-date techniques for FR.Â