IMPLEMENTASI SISTEM PENGENALAN WAJAH UNTUK KEAMANAN AKSES BERBASIS UBUNTU MENGGUNAKAN PYTHON

Security is one of the most important needs for human beings in both the building and the house. For the development of security technology used face recognition. Face recognition is a system that identifies facial features that are capable of detecting familiar faces and unknown faces. In this research is implemented with computer vision where the computer can see and understand so that it is information from an image or video. This computer can also mimic the ability of human intelligence. To classify a face object, OpenCv uses the Haar Cascade classifier and uses Python programming language. Application used face Recognition program is PyCharm Comunity 2018 version 3 with Linux operating system Ubuntu 18.04.2 LTS version. The results showed that the accuracy of face reconition depends on the analysis of OpenCv and the classification of Cascade for computer vision process

NEURAL NETWORK CORRELATION BASED SIMILARITY EVALUATION WITH ZERNIKE MOMENTS FOR THE POSE-INVARIANT FACE RECOGNITION

Human face recognition is best application in pattern recognition for identification and recognition. Development of face recognition system is increasing day by day in market and research organizations. Different parameters and methods are used for face recognition. In this research project, we will discuss about the different algorithms used for face recognition that are Zernike Moments (ZMs) and correlation classification (CC) etc and compare these algorithms with proposed algorithm Z_CC (Zernike with Correlation Classification).The angular information or rotation of the face is calculated by using the Zernike moments (ZM) to obtain the degree or radian of face rotation from the frontal view. The robust combination of angle-invariant and scale-invariant features with the combination of Zernike moments and correlation classification has been proposed with the neural network classification. The experiments will be performed on the variety of datasets. The multi-object dataset has been combined by collection the samples with faces rotated in the training samples. Z_NN (Zernike with neural network) algorithm provide best recognition rate for human face recognition 90%. In this algorithm we use Zernike Moments and correlation for global feature extraction and after that these features are compared by using neural network

Developing and Refining a Multifunctional Facial Recognition System for Older Adults with Cognitive Impairments: A Journey Towards Enhanced Quality of Life

In an era where the global population is aging significantly, cognitive impairments among the elderly have become a major health concern. The need for effective assistive technologies is clear, and facial recognition systems are emerging as promising tools to address this issue. This document discusses the development and evaluation of a new Multifunctional Facial Recognition System (MFRS), designed specifically to assist older adults with cognitive impairments. The MFRS leverages face_recognition [1], a powerful open-source library capable of extracting, identifying, and manipulating facial features. Our system integrates the face recognition and retrieval capabilities of face_recognition, along with additional functionalities to capture images and record voice memos. This combination of features notably enhances the system's usability and versatility, making it a more user-friendly and universally applicable tool for end-users. The source code for this project can be accessed at https://github.com/Li-8023/Multi-function-face-recognition.git.Comment: 10 page

The Office Room Security System Using Face Recognition Based on Viola-Jones Algorithm and RBFN

The university as an educational institution can apply technology in the campus environment. Currently, the security system for office space that is integrated with digital data has been somewhat limited. The main problem is that office space security items are not guaranteed as there might be outsiders who can enter the office. Therefore, this study aims to develop a system using biometric (face) recognition based on Viola-Jones and Radial Basis Function Network (RBFN) algorithm to ensure office room security. Based on the results, the system developed shows that object detection can work well with an object detection rate of 80%. This system has a pretty good accuracy because the object matching success is 73% of the object detected. The final result obtained from this study is a prototype development for office security using face recognition features that are useful to improve safety and comfort for occupants of office space (due to the availability of access rights) so that not everyone can enter the office.The university as an educational institution can apply technology in the campus environment. Currently, the security system for office space that is integrated with digital data has been somewhat limited. The main problem is that office space security items are not guaranteed as there might be outsiders who can enter the office. Therefore, this study aims to develop a system using biometric (face) recognition based on Viola-Jones and Radial Basis Function Network (RBFN) algorithm to ensure office room security. Based on the results, the system developed shows that object detection can work well with an object detection rate of 80%. This system has a pretty good accuracy because the object matching success is 73% of the object detected. The final result obtained from this study is a prototype development for office security using face recognition features that are useful to improve safety and comfort for occupants of office space (due to the availability of access rights) so that not everyone can enter the office

Face recognition using assemble of low frequency of DCT features

Face recognition is a challenge due to facial expression, direction, light, and scale variations. The system requires a suitable algorithm to perform recognition task in order to reduce the system complexity. This paper focuses on a development of a new local feature extraction in frequency domain to reduce dimension of feature space. In the propose method, assemble of DCT coefficients are used to extract important features and reduces the features vector. PCA is performed to further reduce feature dimension by using linear projection of original image. The proposed of assemble low frequency coefficients and features reduction method is able to increase discriminant power in low dimensional feature space. The classification is performed by using the Euclidean distance score between the projection of test and train images. The algorithm is implemented on DSP processor which has the same performance as PC based. The experiment is conducted using ORL standard face databases the best performance achieved by this method is 100%. The execution time to recognize 40 peoples is 0.3313 second when tested using DSP processor. The proposed method has a high degree of recognition accuracy and fast computational time when implemented in embedded platform such as DSP processor

Researching a machine learning algorithm for a face recognition system

This article investigated the problem of using machine learning algorithms to recognize and identify a user in a video sequence. The scientific novelty lies in the proposed improved Viola-Jones method, which will allow more efficient and faster recognition of a person's face. The practical value of the results obtained in the work is determined by the possibility of using the proposed method to create systems for human face recognition. A review of existing methods of face recognition, their main characteristics, architecture and features was carried out. Based on the study of methods and algorithms for finding faces in images, the Viola-Jones method, wavelet transform and the method of principal components were chosen. These methods are among the best in terms of the ratio of recognition efficiency and work speed. Possible modifications of the Viola-Jones method are presented. The main contribution presented in this article is an experimental study of the impact of various types of noise and the improvement of company security through the development of a computer system for recognizing and identifying users in a video sequence. During the study, the following tasks were solved: – a model of face recognition is proposed, that is, the system automatically detects a person's face in the image (scanned photos or video materials); – an algorithm for analyzing a face is proposed, that is, a representation of a person's face in the form of 68 modal points; – an algorithm for creating a digital fingerprint of a face, which converts the results of facial analysis into a digital code; – development of a match search module, that is, the module compares the faceprint with the database until a match is foun

Development of a manufacturability analysis system for reinforced plastics components.

This thesis describes the research and development of a systematic and consistent methodology to perform manufacturability analysis of Reinforced Plastic Parts (RPP). The proposed methodology evaluates the part model in the early stages of the product development process considering the capabilities and constraints of available manufacturing processes, materials and tooling required in standard RPP production. Critical Manufacturing Part Features (CMPF) are identified and the relationship between the model's geometrical information, the expert's geometric reasoning, and the knowledge about the involved manufacturing processes are clarified and set together in an efficient feature-rule-based manufacturability analysis system. The prototype system named 'FEBAMAPP', combines solid modelling (SM), automatic feature recognition (AFR), object oriented programming (OOP), and a rule-based system (RBS) in order to assess the manufacturability of the proposed design. The novelty of this research is based in the use of a Face Vector (FVector) concept to transform geometrical and topological information of the solid model into a suitable input data to be used in the Neural Network Feature Recognition System. Further novelty arises from the fact that this is the first attempt to use neural networks in the recognition of 3-D features in hollow parts including the presence of fillets along the edges of the part. The manufacturability evaluation can be performed considering different combinations of materials along with different manufacturing processes giving the designer the opportunity of selecting an appropriate combination for any specific application. Promising results have been obtained during the test of the system, where 100 % recognition of trained features with 90% confidence has been achieved. Also, good results have been obtained in the recognition of non-trained features such as the Cross-Slot feature, which is recognised as a Slot feature. After automatic feature recognition, Manufacturability Analysis is focused on internal and external characteristics of the model's features, where potential manufacturing difficulties are identified and feedback in terms of design suggestions is then used to advise the design process and improve the overall manufacturability of the part. This manufacturability evaluation in terms of internal and external characteristics of the features has proved to be efficient in detecting detailed design errors that can be costly in further manufacturing stages in the product development process

Drive in Peace

In this paper, in order to implement a computer vision-based recognition system of driving fatigue. In addition to detecting human face in different light sources and the background conditions, and tracking eyes state combined with fuzzy logic to determine whether the driver of the physiological phenomenon of fatigue from face of detection. Driving fatigue recognition has been valued highly in recent years by many scholars and used extensively in various fields, for example, driver activity tracking, driver visual attention monitoring, and in-car camera systems.In this paper, we use the Windows operating system as the development environment, and utilize PC as the hardware platform. First, the system uses a camera to obtain the frame with a human face to detect, and then uses the frame to set the appropriate skin color scope to find face. Next, we find and mark out the eyes and the lips from the selected face area. Finally, we combine the image processing of eyes features with fuzzy logic to determine the driver's fatigue level, and make the graphical man-machine interface with MiniGUI for users to operate.Along with that we are using Arduino Uno microcontroller which is connected to MQ2-smoke sensor through which we can detect smoke which appears through issue in the car system. The results of experiment show that we achieve this system on PC platform successfully

A smart environment for biometric capture

The development of large scale biometric systems require experiments to be performed on large amounts of data. Existing capture systems are designed for fixed experiments and are not easily scalable. In this scenario even the addition of extra data is difficult. We developed a prototype biometric tunnel for the capture of non-contact biometrics. It is self contained and autonomous. Such a configuration is ideal for building access or deployment in secure environments. The tunnel captures cropped images of the subject's face and performs a 3D reconstruction of the person's motion which is used to extract gait information. Interaction between the various parts of the system is performed via the use of an agent framework. The design of this system is a trade-off between parallel and serial processing due to various hardware bottlenecks. When tested on a small population the extracted features have been shown to be potent for recognition. We currently achieve a moderate throughput of approximate 15 subjects an hour and hope to improve this in the future as the prototype becomes more complete