AI DEPLOYMENT IN FACE DETECTION AND FACE RECOGNITION
MODEL BY IMPLEMENTING COMPUTER VISION

Sistem dapat menggunakan IP Camera maupun CCTV, IP Camera membutuhkan kabel UTP untuk melakukan komunikasi data, sementara CCTV membutuhkan kabel Coaxial. Pengenalan wajah dilakukan melalui tahap Face Detection, Feature Extraction dan Face Recognition, selanjutnya dicocokkan dengan data profil yang tersimpan di dalam Database. Untuk mendeteksi wajah diperlukan OpenCV yang ditanamkan ke dalam sistem. OpenCV adalah sebuah library (perpustakaan) yang digunakan untuk mengolah gambar dan video hingga kita mampu mengekstrak informasi di dalamnya. OpenCV dapat berjalan di berbagai bahasa pemrograman, seperti C, C++, Java, Python, dan juga didukung di berbagai platform seperti Windows, Linux, Mac OS, iOS dan Android. Setiap pengguna sistem Absensi Face Recognition perlu dilakukan registrasi terlebih dahulu 1 (satu) persatu, dan sistem melakukan Training dari video setiap pengguna yang didaftarkan dan dibuat Source Base dalam bentuk foto dan disimpan di komputer server sebagai menjadi pembanding dan mendeteksi wajah dari berbagai sudut kamera nantinya. Database digunakan adalah MySQL dengan data yang ditampung adalah informasi data wajah, data jadwal, data User serta data informasi absensi. Koneksi untuk CCTV menggunakan RTSP yang merupakan jaringan komputer yang dirancang untuk kebutuhan multimedia dan sistem komunikasi data, yang dapat yang dapat mengendalikan aliran media dari server. Protokol ini digunakan untuk menetapkan dan mengendalikan sesi media antara dua titik ujungnya. Sebagian besar server RTSP menggunakan Real-time Transport Protocol (RTP) yang saling melengkapi dengan Real-time Control Protocol (RTCP) untuk pengiriman aliran media. Sementara itu penggunaan IP Camera atau Kamera IP adalah kamera dengan basis Internet Protocol, jenis kamera video digital yang menerima data kontrol dan mengirimkan data gambar melalui jaringan IP. biasanya digunakan untuk pengawasan tetapi berbeda dengan kamera analog Closed-circuit Television (CCTV), yang mana tidak memerlukan perangkat perekaman lokal, namun hanya jaringan area lokal. Kebanyakan kamera IP adalah Webcam, tetapi istilah kamera IP atau Netcam biasanya hanya berlaku untuk kamera yang dapat langsung diakses melalui koneksi jaringan dan dapat digunakan untuk kamera pengawasan.

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Face Detection and Recognition Algorithm in Digital Image Based on Computer Vision Sensor

Journal of Sensors ◽

10.1155/2021/4796768 ◽

2021 ◽

Vol 2021 ◽

pp. 1-16

Author(s):

Di Lu ◽

Limin Yan

Keyword(s):

Computer Vision ◽

Face Recognition ◽

Face Detection ◽

Data Communication ◽

Electrical Signal ◽

Network Technology ◽

Skin Texture ◽

Advantages And Disadvantages ◽

Detection Technology ◽

Detection And Recognition

With the continuous innovation of network technology, various kinds of convenient network technologies have grown, and human dependence on network technology has gradually increased, which has resulted in the importance of network information security issues. With the continuous development of my country’s industrialization, the application of sensors is becoming more and more extensive, for example, the security vulnerabilities and defects in the operating system itself. Traditional sensors can “perceive” a certain thing or signal, convert it into an electrical signal and record it, and then use a conversion circuit to output the electrical signal into a value or other display form that is conducive to observation. Nowadays, sensors have been further developed. Based on the original “perception” function, combined with computer technology, it integrates data storage, data processing, data communication, and other functions, so that it has analysis functions and can better display information. The technical level has reached a new level. Early intelligent recognition mainly used the uniqueness of finger and palm lines to scan and contrast, but due to some weather reasons or skin texture constraints caused by skin texture, these methods showed certain limitations. This paper proposes a new computer vision-based algorithm from face detection technology and face recognition technology. In the face detection technology, it is mainly introduced from the OpenCV method. Face recognition technology is improved in practical applications through the Seetaface method and YouTu method. At the same time, using the contrast experiment, the detection and recognition rates under the three different requirements of side face detection, occlusion detection, and facial exaggerated expression are compared, and the accuracy of each method is improved. The results show that each case is compared in each case. The advantages and disadvantages of the algorithm effectively verify the effectiveness of the method.

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Face Recognition and Face Detection Using Open Computer Vision Classifiers and Python

Advances in Data Mining and Database Management - Intelligent Analytics With Advanced Multi-Industry Applications ◽

10.4018/978-1-7998-4963-6.ch009 ◽

2021 ◽

pp. 185-207

Author(s):

Priyank Jain ◽

Meenu Chawla ◽

Sanskar Sahu

Keyword(s):

Computer Vision ◽

Face Recognition ◽

Face Detection ◽

Local Binary Pattern ◽

Research Work ◽

Modern World ◽

The Face ◽

Haar Cascade

Identification of a person by looking at the image is really a topic of interest in this modern world. There are many different ways by which this can be achieved. This research work describes various technologies available in the open-computer-vision (OpenCV) library and methodology to implement them using Python. To detect the face Haar Cascade are used, and for the recognition of face eigenfaces, fisherfaces, and local binary pattern, histograms has been used. Also, the results shown are followed by a discussion of encountered challenges and also the solution of the challenges.

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Pig Face Recognition Model Based on a Cascaded Network

Applied Engineering in Agriculture ◽

10.13031/aea.14482 ◽

2021 ◽

Vol 37 (5) ◽

pp. 879-890

Author(s):

Rong Wang ◽

ZaiFeng Shi ◽

Qifeng Li ◽

Ronghua Gao ◽

Chunjiang Zhao ◽

...

Keyword(s):

Face Recognition ◽

Face Detection ◽

Individual Recognition ◽

Recognition Performance ◽

Individual Identification ◽

List Type ◽

Recognition Model ◽

Model Based ◽

Face Images ◽

Cascaded Model

HighlightsA pig face recognition model that cascades the pig face detection network and pig face recognition network is proposed.The pig face detection network can automatically extract pig face images to reduce the influence of the background.The proposed cascaded model reaches accuracies of 99.38%, 98.96% and 97.66% on the three datasets.An application is developed to automatically recognize individual pigs.Abstract. The identification and tracking of livestock using artificial intelligence technology have been a research hotspot in recent years. Automatic individual recognition is the key to realizing intelligent feeding. Although RFID can achieve identification tasks, it is expensive and easily fails. In this article, a pig face recognition model that cascades a pig face detection network and a pig face recognition network is proposed. First, the pig face detection network is utilized to crop the pig face images from videos and eliminate the complex background of the pig shed. Second, batch normalization, dropout, skip connection, and residual modules are exploited to design a pig face recognition network for individual identification. Finally, the cascaded network model based on the pig face detection and recognition network is deployed on a GPU server, and an application is developed to automatically recognize individual pigs. Additionally, class activation maps generated by grad-CAM are used to analyze the performance of features of pig faces learned by the model. Under free and unconstrained conditions, 46 pigs are selected to make a positive pig face dataset, original multiangle pig face dataset and enhanced multiangle pig face dataset to verify the pig face recognition cascaded model. The proposed cascaded model reaches accuracies of 99.38%, 98.96%, and 97.66% on the three datasets, which are higher than those of other pig face recognition models. The results of this study improved the recognition performance of pig faces under multiangle and multi-environment conditions. Keywords: CNN, Deep learning, Pig face detection, Pig face recognition.

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Human Face Recognition using LBPH

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.f8117.038620 ◽

2020 ◽

Vol 8 (6) ◽

pp. 3208-3212

Keyword(s):

Computer Vision ◽

Face Recognition ◽

Face Detection ◽

Local Binary Pattern ◽

Human Face Recognition ◽

Smart Systems ◽

Finger Print ◽

The Face ◽

Face Detection And Recognition ◽

Frame Work

During the beginning of seventieth centuries, human facial recognition has become one among the researched areas in the area of finger print scanning and computer vision. Identifying a person with an image has been popularized through the mass media. The recent technologies are totally focusing on developing the smart systems that will recognize the faces for biometric purposes. In this context automatic face recognition is applied for security purposes to find the criminal, attendance system, scientific laboratories etc. This research paper presents the frame work for real time face detection. However, it is less robust to finger print or retina scanning. This paper describes about the face detection and recognition. These technologies are available in the Open-Computer-Vision (OpenCV) library and methodology to implement them using Python in image processing and machine learning. For face detection, Haar-Cascades algorithms were used and for face recognition the algorithm like Eigen faces, and Local binary pattern histograms were used.

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Face with Mask Detection and Recognition for Smart Attendance System

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.36615 ◽

2021 ◽

Vol 9 (VII) ◽

pp. 1586-1591

Author(s):

Shweta Panjabrao Dhawale

Keyword(s):

Computer Vision ◽

Deep Learning ◽

Face Recognition ◽

Face Detection ◽

Face Mask ◽

Learning Framework ◽

The Face ◽

Face Detection And Recognition ◽

Python Programming ◽

Detection And Recognition

In this paper we will see the face mask detection and recognition for smart attendance system. In current pandemic situation our proposed system is very useful. We have used here face algorithm technique, python programming and to capture the images open cv is used., open cv2 now comes with a very new face recognizer class for the face recognition and popular computer vision liberaay started by intel in 1999. Open cv released under BSD licence that’s why used in the academic projects. We have used the concept of deep learning framework for the detection of faces. our aim is to present the study of previous attempts at face detection and recognition for smart attendance system by using deep learning .these is rapidly growing technology with its application in various aspects.

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Face recognition for automatic vehicle ignition based on Raspberry Pi

REVISTA ODIGOS ◽

10.35290/ro.v1n2.2020.326 ◽

2020 ◽

Vol 1 (2) ◽

pp. 53-68

Author(s):

Alex V. Nuñez ◽

Liliana N. Nuñez

Keyword(s):

Computer Vision ◽

Face Recognition ◽

Face Detection ◽

Facial Recognition ◽

Data Gathering ◽

Software Component ◽

Security System ◽

Raspberry Pi ◽

Hardware Platform ◽

And Training

In this project a facial recognition application for automatic vehicle ignition is developed. This application is built using a Raspberry Pi as the hardware platform and the OpenCV library for computer vision as the software component. In this research the different methods for automobile security are analyzed, as well as, the different methods used to perform face recognition. The main goal of this application is to enhance the security system of the vehicle, allowing to ignite the vehicle only by register users. To achieve this goal three main processes are carried out, face detection, data gathering, and training the system to grant access through face recognition.

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Face Recognition

Advances in Systems Analysis, Software Engineering, and High Performance Computing - Emerging Research Surrounding Power Consumption and Performance Issues in Utility Computing ◽

10.4018/978-1-4666-8853-7.ch020 ◽

2016 ◽

pp. 405-425 ◽

Cited By ~ 1

Author(s):

Alexander Alling ◽

Nathaniel R Powers ◽

Tolga Soyata

Keyword(s):

Face Recognition ◽

Real Time ◽

Face Detection ◽

Database Search ◽

Recognition Model ◽

Detailed Explanation ◽

Computer Resources ◽

Gpu Architecture ◽

Conventional Computer

Face recognition is a sophisticated problem requiring a significant commitment of computer resources. A modern GPU architecture provides a practical platform for performing face recognition in real time. The majority of the calculations of an eigenpicture implementation of face recognition are matrix multiplications. For this type of computation, a conventional computer GPU is capable of computing in tens of milliseconds data that a CPU requires thousands of milliseconds to process. In this chapter, we outline and examine the different components and computational requirements of a face recognition scheme implementing the Viola-Jones Face Detection Framework and an eigenpicture face recognition model. Face recognition can be separated into three distinct parts: face detection, eigenvector projection, and database search. For each, we provide a detailed explanation of the exact process along with an analysis of the computational requirements and scalability of the operation.

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