Facial Recognition For Effective Transactions in E-Business

In today’s world many times need the human identification for the purpose of security of information, authentication, search criminals, find missing child. The biometric recognition uses for human identification because the biometrical characteristics are unique, and it’s based on two types are behavioural and physiological characteristics. This paper include biometric recognition system overview fingerprint recognition, face recognition, iris recognition, speech recognition and gesture recognition. overview include the various types of algorithm using in biometric recognition system are discrete cosine transform, key local variation, minutiae matching algorithm, PCA, active contour algorithm, fuzzy cmeans clustering, LPCC Algorithm, LDA Algorithm and Fuzzy Neural Network. The standard techniques implement following some of algorithms and recognition rate is near prediction about 100%. Keywords: Biometric Recognition, LDA, PCA Algorithms, Database.

Traditional identity verification of students based on the human proctoring approach can cause a scam identity verification and ineffective processing time, particularly among vast groups of students. Most student identification cards outdated personal information. Several biometric recognition approaches have been proposed to strengthen students’ identity verification. Most educational adoption technologies struggle with evaluation and validation techniques to ensure that biometric recognition systems are unsuitable for utilization and implementation for student identity verification. This study presents the internet of things to develop flexible biometric recognition systems and an approach to assess the quality of biometric systems for educational use by investigating the effectiveness of identity verification of various biometric recognition technologies compared to the traditional verification method. The unimodal, multimodal, and semi-multimodal biometric technologies were tested using the developed internet of things-base biometric recognition systems examined by applying the proposed quality metrics of scoring factors based on accuracy, error rate, processing time, and cost. Hundreds of undergraduate exam takers were a sample group. Key findings indicate that the designed and presented systems suitably attain identity verification of exam students using a unimodal biometric. The unimodal facial biometric system promises excellent support. A unimodal fingerprint biometric system ensures second excellent aid for student identity verification. However, multimodal and semi-multimodal biometric systems provide better accuracy with fewer handling times and higher costs. This study contributes significantly to the knowledge of utilizing biometric recognition for identity verification in smart educational applications.

Attending classes by students is associated with the assimilation of educational material by students and the ability to plan and organize activities. However, at present in educational institutions, as a rule, student attendance is recorded manually. Activities are performed frequently and repeatedly, thus wasting instructors' study time. Additionally, the face is one of the most widely used biometric characteristics for personal identification so an automated attendance system using face recognition has been proposed. In recent years, convolutional neural networks (CNN) have become the dominant deep le11arning method for face recognition. In this article, the features of building an automated student attendance system by biometric face recognition using the convolution neural network modelhas been discussed. Analyzed and solved the main tasks that arise when building an automated student attendance monitoring system: creating a dataset of students' face images; building and training a biometric face recognition model; face recognition from the camera and registration in the database; extension to the face image dataset. The use of the capabilities of the Python and OpenCV libraries is shown. The conducted testing of the accuracy of the developed CNN model of biometric face recognition showed good results – the overall accuracy score is not less than 0.75. The developed automated student attendance monitoring system in classrooms can be used to determine student attendance in different forms of the educational process. Its implementation will significantly reduce the monitoring time and reduce the number of errors in maintaining attendance logs. The introduction of an automated attendance monitoring system will significantly improve the organization of the educational process to ensure its quality.

Biometric face recognition presents a wide range of variability sources, such as make up, illumination, pose, facial expression, etc. In this paper we use the Japanese Female Facial Expression Database (JAFFE) in order to evaluate the influence of facial expression in biometric recognition rates. In our experiments we used a nearest neighbor classifier with different number of training samples, different error criteria, and several feature extractions. Our experimental results reveal that some facial expressions produce a recognition rate drop, but the optimal length of the feature extracted vectors is the same with the presence of facial expressions than with neutral faces.KeywordsFacial ExpressionFace RecognitionDiscrete Cosine TransformMean Absolute DifferenceBiometric SystemThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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 technology is an increasingly growing, constantly adapting technology. It can serve many benefits for legal entities in many ways. Examples of this would be biometric recognition or border control at an airport. Because of its power and efficiency, many legal entities are looking to utilise this technology. With this continued growth comes ethical concerns about ensuring data privacy for individuals whose data is collected, used, and stored to power this technology. Currently, there is a lack of an ethical framework for legal entities to adopt, follow and utilise within New Zealand. This makes it an ethical grey area for the businesses and individuals looking to utilise this technology. This document aims to review existing frameworks about facial recognition technology,finally concluding a code of ethics for facial recognition use in New Zealand for the engineering profession.

This project presents a Home Automation Security System that integrates face detection and recognition with RFID technology using a Raspberry Pi. The system aims to enhance security and access control in homes by automating the process of user authentication through facial recognition and RFID tags. The system utilizes a Raspberry Pi as the central control unit, a camera module for face detection and recognition, and an RFID reader to scan RFID tags for additional access control. The system functions by scanning an RFID tag, and upon detection of a valid tag, it activates the camera to capture the face of the person requesting access. The captured image is then processed using OpenCV and face_recognition libraries in Python to match the face with a pre-stored database of authorized individuals. If both the RFID tag and face match the stored data, the system grants access by performing automation actions such as unlocking doors or turning on lights. Keywords- Raspberry Pi, Pi Camera, Face Detection, Face Recognition, RFID Technology, RFID Tag Verification.

In the study, ECG is investigated as a biometric recognition system. To fit in the real-life scenarios, different physiological conditions and signal compression are considered. A total of 9 subjects conducting three simple exercises which are riding bicycle on high resistance, low resistance and walking taken from Physionet. Due to the noise collected during ECG recording process, the contaminated signals are filtered using low pass Butterworth filter. Then, QRS is segmented using Pan Tompkins algorithm. After that, Wavelet Transform is applied to compress the segmented QRS. Classification is done on non-compressed and compressed QRS by using multi-class of SVM to compare its accuracy. The study discovered that, biometric can be performed on compressed signals. The outcome also shows that, Cubic SVM is the proper classification technique to be applied on person verification regardless of different physiological conditions. The accuracy achieved was up to 94.4% for non-compressed signals and 91.7% for compressed signals.

An android based course attendance system using face recognition

Modern biometric systems have relied on face recognition as it is precise, convenient and unobtrusive. However, standard visible-spectrum face recognition systems are very much sensitive to changes in lighting, facial expressions, and surroundings and at the same time have no ability to conduct physiological measurements in tandem. Within the framework of access control and overall safety needs in the post-pandemic context, these restrictions have indicated a gap in research that is urgent: the lack of integrated frameworks that carry out the functions of identity verification and health-related screening in tandem with each other. This article is a critical survey of the state-of-the-art in the field of thermal-sensitive face recognition, which integrates the visible RGB imaging with biometric recognition and long-wave infrared (LWIR) thermal sensing with the purpose of estimating body temperature. We syntactically examine the available architectures, sensing configurations, fusion strategies, datasets, and evaluation protocols found in the literature. The review emphasizes the fact that dual-modal systems have the potential to allow real-time and contactless identity verification and support a large-scale approach in high-traffic settings. Moreover, this paper addresses the main technical and practical issues that are limiting the large-scale implementation, such as sensor calibration, cross-modular data alignment, environmental bias, data privacy, as well as ethical factors. Last but not the least, we describe the new research directions and future outlooks of the unified biometric and health-conscious access control systems through a critical lens that analyzes performance trade-offs, computational costs, and ethical implications, with the aim of informing the researchers and practitioners of the creation of effective, scalable, and privacy-aware solutions.

Face recognition is biometric pattern recognition, which is more acceptable and convenient for users compared with other biometric recognition traits. Among many problems in face recognition system, pose problem is still considered as one of the major problem still unsolved in satisfactory level. This paper proposes a novel pose tolerant face recognition approach. Proposed system's contribution is a novel and efficient approach that includes feature extraction, pose transformation learning and recognition stages. In first stage, 2D PCA is used as robust feature extraction technique. The linear regression is used as efficient and accurate transformation learning technique to create frontal face image from a different posed face images in the second stage. In last stage, Mahalanobis distance is used for recognition. Experiments on FERET and FEI face databases demonstrate higher performance in comparison with traditional systems.

Vehicle security is a big issue worldwide. Vehicle manufacturing company provides ac-tive/passive RFID based remote key for door entry-level, and same is used engine ignition. Us-ing the master RFID signal reader device can read the active/passive signal remotely which are available in open market, this situation is very vulnerable to vehicle safety. The advantage of a multilayer authentication system is, it provides protection at entry level, So, if a door is not opened, neither engine turning on nor vehicle movement is possible, goods and personal be-longiness inside a vehicle remain safe. A smart authentication system is developed with a Rasp-berry pi development board, which is interfaced with fingerprint biometric, and camera mod-ules. This multilayer vehicle door security system provides an option for the use of anyone or both authentication level in logically ANDing mode, such as a driver can be utilizing security system for smart key, finger biometric, and face recognition. Multilayer authentication will re-strict unauthorized entry into the vehicle. The owner of the vehicle can set and use a multilayer authentication system as per the requirement. Once this system is installed in a vehicle, it will provide maximal level security at the preliminary stage as restricted entry from the door. In a laboratory environment, system implementation and testing were very successfully carried out and provided satisfying results. Multilayer authentication for Vehicle Door Access using Open-source devices will reduce the number of crimes significantly related to vehicle theft and unau-thorized access of the vehicle.

Introduction to face recognition and evaluation of algorithm performance

Face recognition is still a challenging problem as there is a high possibility that the differences existing within a person or subject can exceed the differences present between different persons. Most of the current research work in the field of biometric face recognition has dealt with (Red-GreenBlue) RGB images. However, Hyper Spectral Imaging (HSI), introduces the spectral dimension for improved discrimination and leads to building a more efficient face recognition system. The spectral dimension adds more intricate details to the image. As a result hyper spectral imaging helps in improving face recognition accuracy. The faces are captured at varying spectral wavelengths of the electromagnetic spectra. Hyper spectral imaging often increases facial discrimination by capturing more biometric measurements and thus revealing information that is not revealed by the commonly used RGB images. The proposed methodology for face recognition using hyper spectral imaging includes performing band selection and band fusion on the hyper spectral face cubes and then classifying them using 3 Dimensional Convolution Neural Networks (3D-CNN). The classification using 3D-CNN gave a promising accuracy of 97.3%.

This work proposes an equipment oriented to measuring the alcoholic level and simultaneously applying face recognition for people who enter risk places where their physical integrity can be affected due to their drunkenness state. In the state of the art, it is verified that several methods of measuring breathalyzer do not integrate the simultaneous facial recognition for the purposes of proper personnel access control and registration. It is also verified that subjective methods are applied such as the emitted smell perception, gait, the way of speaking or behavioral aspects. The proposed equipment consists of electronic devices that allow the detection of air flow and the measurement of the alcoholic level through a reduced board computer. Biometric face recognition is carried out through image processing algorithms, convolutional neural networks and support vector machines SVM, which run on a computer which is synchronized with the measurement equipment. The computer registers the recognized person in a database with the associated detected alcoholic level. For the validation of the proposed equipment, several samples of alcoholic level, delay times in the acquisition of images and the face recognition rate were evaluated. Alcohol level measurements were compared with those obtained through a certified digital breathalyzer. In this validation, Pearson's correlation coefficient was used, obtaining a value of 0.937. The maximum time delay in capturing the image during the emission of the airflow by the person was 0.067 s, while the percentage of true face recognition was higher than 95%.