scholarly journals Penerapan Face Recognition pada Aplikasi Akademik Online

2021 ◽  
Vol 5 (4) ◽  
pp. 420
Author(s):  
Budi Tri Utomo ◽  
Iskandar Fitri ◽  
Eri Mardiani
Keyword(s):  
Face Recognition ◽  
Local Binary Pattern ◽  
Biometric Identification ◽  
Grayscale Image ◽  
Face Database ◽  
Identification Process ◽  
National University ◽  
Haar Cascade ◽  
Pattern Histogram ◽  
Rgb Image

In the era of big data, the biometric identification process is growing very fast and is increasingly being implemented in many applications. Face recognition technology utilizes artificial intelligence (AI) to recognize faces that are already stored in the database. In this research, it is proposed to design an online academic login system at the National University using real time face recognition used OpenCV with the Local Binary Pattern Histogram algorithm and the Haar Cassade Classification method. The system will detect, recognize and compare faces with the stored face database. The image used is 480 x 680 pixels with a .jpg extension in the form of an RGB image which will be converted into a Grayscale image., to make it easier to calculate the histogram value of each face that will be recognized. With a modeling system like this it is hope to make it easy for user to log into online academics.Keywords:Face Recognition, Haar Cascade Clasifier, Local Binary Pattern Histogram, Online Akademic, OpenCV. 

scholarly journals Multi-Object Face Recognition Using Local Binary Pattern Histogram and Haar Cascade Classifier on Low-Resolution Images

2021 ◽  
Vol 11 (1) ◽  
pp. 45-58
Author(s):  
R. Rizal Isnanto ◽  
Adian Rochim ◽  
Dania Eridani ◽  
Guntur Cahyono
Keyword(s):  
Face Recognition ◽  
Real Time ◽  
Local Binary Pattern ◽  
Video Data ◽  
Cascade Classifier ◽  
Low Resolution ◽  
The Face ◽  
Haar Cascade ◽  
Pattern Histogram ◽  
Low Resolution Images

This study aims to build a face recognition prototype that can recognize multiple face objects within one frame. The proposed method uses a local binary pattern histogram and Haar cascade classifier on low-resolution images. The lowest data resolution used in this study was 76 × 76 pixels and the highest was 156 × 156 pixels. The face images were preprocessed using the histogram equalization and median filtering. The face recognition prototype proposed successfully recognized four face objects in one frame. The results obtained were comparable for local and real-time stream video data for testing. The RR obtained with the local data test was 99.67%, which indicates better performance in recognizing 75 frames for each object, compared to the 92.67% RR for the real-time data stream. In comparison to the results obtained in previous works, it can be concluded that the proposed method yields the highest RR of 99.67%.

scholarly journals Face Recognition System for Prevention of Car Theft with Haar Cascade and Local Binary Pattern Histogram using Raspberry Pi

2020 ◽  
pp. 407-425
Author(s):  
Muhammad Hanif Abdurrahman ◽  
Haryadi Amran Darwito ◽  
Akuwan Saleh
Keyword(s):  
Face Recognition ◽  
Local Binary Pattern ◽  
Recognition System ◽  
Raspberry Pi ◽  
Face Recognition System ◽  
Haar Cascade ◽  
The Difference ◽  
Cascade Method ◽  
Car Security ◽  
Pattern Histogram

In this era, the occurrence of vehicle theft has become a fairly frequent problem, especially in big cities like Jakarta and Surabaya. Although the technology for car security is very sophisticated (e.g. keyless system), but there are many cases that thieves still can break into the system. Once a car was stolen, the whereabouts of the car was unknown and the thief was on the loose. The goal of this research is to overcome this problem. As a device, this research works on Raspberry Pi 3 that connected with the Raspberry Pi Camera. Using the OpenCV library, with the Haar Cascade method for face detection, and Local Binary Pattern Histogram for face recognition. The device must be connected to the internet in order to send the information using a Telegram message. The research results show the success of the device system in face-recognizing between the car owner and car thief with optimal conditions in the morning until the afternoon with the light intensity around 660 to 1000 lux, and best recognizing distance at 50 cm. The success rate for obtaining the car’s location for the outdoor condition is 100%. Even if there is a slope or an error data, it can be tolerated because the difference was not too high, about 0.1-1.0 m.

scholarly journals Face Recognition in an Unconstrained and Real-Time Environment Using Novel BMC-LBPH Methods Incorporates with DJI Vision Sensor

2020 ◽  
Vol 9 (4) ◽  
pp. 54
Author(s):  
Md Manjurul Ahsan ◽  
Yueqing Li ◽  
Jing Zhang ◽  
Md Tanvir Ahad ◽  
Munshi Md. Shafwat Yazdan
Keyword(s):  
Face Recognition ◽  
Real Time ◽  
Local Binary Pattern ◽  
Computation Time ◽  
Threshold Value ◽  
Light Illumination ◽  
Rainy Weather ◽  
Face Datasets ◽  
Bad Weather ◽  
Pattern Histogram

Face recognition (FR) in an unconstrained environment, such as low light, illumination variations, and bad weather is very challenging and still needs intensive further study. Previously, numerous experiments on FR in an unconstrained environment have been assessed using Eigenface, Fisherface, and Local binary pattern histogram (LBPH) algorithms. The result indicates that LBPH FR is the optimal one compared to others due to its robustness in various lighting conditions. However, no specific experiment has been conducted to identify the best setting of four parameters of LBPH, radius, neighbors, grid, and the threshold value, for FR techniques in terms of accuracy and computation time. Additionally, the overall performance of LBPH in the unconstrained environments are usually underestimated. Therefore, in this work, an in-depth experiment is carried out to evaluate the four LBPH parameters using two face datasets: Lamar University data base (LUDB) and 5_celebrity dataset, and a novel Bilateral Median Convolution-Local binary pattern histogram (BMC-LBPH) method was proposed and examined in real-time in rainy weather using an unmanned aerial vehicle (UAV) incorporates with 4 vision sensors. The experimental results showed that the proposed BMC-LBPH FR techniques outperformed the traditional LBPH methods by achieving the accuracy of 65%, 98%, and 78% in 5_celebrity dataset, LU dataset, and rainy weather, respectively. Ultimately, the proposed method provides a promising solution for facial recognition using UAV.

scholarly journals Physiological Traits (Face Recognition) Based Door Lock

2019 ◽  
Vol 8 (4) ◽  
pp. 5808-5812
Keyword(s):  
Face Recognition ◽  
Local Binary Pattern ◽  
Technological Advancement ◽  
Web Based ◽  
Prime Concern ◽  
New Development ◽  
Hardware Component ◽  
Door Lock ◽  
Live Image ◽  
Pattern Histogram

Security is now a prime concern for any individual in modern days. Theever-increasing graph of technological advancement in the field of Internet of things and other arenas have paved way for new development of smart web-based locking system which is based on face recognition for authentication. The proposed system uses a feature similar to Haar for the purpose of face detection and also Local Binary Pattern Histogram (LBPH). The project also extends its usability by sending live image of the guest which arrives and can even send a notification on the phone to the owner. The proposed system can be embedded along with other technologies to form a smart housing. The implementation of the project is done using Arduino board, python for programming, Open CV library is also included, and the hardware component also includes camera module for face recognition.

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

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.

scholarly journals Real-time mask detection and face recognition using eigenfaces and local binary pattern histogram for attendance system

2021 ◽  
Vol 10 (2) ◽  
pp. 1105-1113
Author(s):  
Mohd Suhairi Md Suhaimin ◽  
Mohd Hanafi Ahmad Hijazi ◽  
Chung Seng Kheau ◽  
Chin Kim On
Keyword(s):  
Face Recognition ◽  
Real Time ◽  
Local Binary Pattern ◽  
Recognition System ◽  
Report Generation ◽  
Face Recognition System ◽  
The Face ◽  
Common Face ◽  
Future Work ◽  
Pattern Histogram

Face recognition is gaining popularity as one of the biometrics methods for an attendance system in an organization. Due to the pandemic, the common face recognition system needs to be modified to meet the current needs, whereby facemask detection is necessary. The main objective of this paper is to investigate and develop a real-time face recognition system for the attendance system based on the current scenarios. The proposed framework consists of face detection, mask detection, face recognition, and attendance report generation modules. The face and facemask detection is performed using the haar cascade classifier. Two techniques for face recognition were investigated, the eigenfaces and local binary pattern histogram. The initial experimental results and implementation at Kuching Community College show the effectiveness of the system. For future work, an approach that is able to perform masked face recognition will be investigated.

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