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 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.

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 Penerapan Face Recognition Pada Aplikasi Akademik Online

2020 ◽  
Vol 16 (3) ◽  
pp. 195
Author(s):  
Budi Tri Utomo ◽  
Iskandar Fitri ◽  
Eri Mardiani
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Face Recognition ◽  
Real Time ◽  
Local Binary Pattern ◽  
Pattern Histogram

Di era big data seperti sekarang ini, proses identifikasi biometrik berkembang dengan sangat cepat dan semakin banyak diimplementasikan pada banyak aplikasi. Teknologi pengenalan wajah memanfaatkan kecerdasan artificial intelligence (AI) untuk mengenali wajah. Didalam penelitian ini diajukan sebuah perancangan sistem login akademik online di Universitas Nasional dengan memanfaatkan face recognition secara real time yang berbasis OpenCV dengan algoritma Local Binary Pattern Histogram, dan metode Haar Cassade Clasifier. Sistem akan mendeteksi, mengenali dan menbandingkan wajah yang tertangkap kamera dengan database wajah yang tersimpan. Citra gambar wajah yang digunakan berukuran 480 x 680 pixel berekstensi .jpg dalam bentuk citra RGB yang akan dirubah menjadi citra Grayscale, untuk mempermudah perhitungan nilai histrogram dari setiap wajah yang akan dikenali. Dengan pemodelan sistem seperti ini diharapkan dapat mempermudah mahasiswa untuk login ke akademik online.

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.

scholarly journals Using Median-LBPH Algorithm for Real-Time Face Recognition System

2021 ◽  
Vol 9 (11) ◽  
pp. 1497-1503
Author(s):  
Lohith Raj S N
Keyword(s):  
Face Recognition ◽  
Law Enforcement ◽  
Real Time ◽  
Web Application ◽  
Computation Time ◽  
Recognition System ◽  
Text Messages ◽  
Law Enforcement Agencies ◽  
Face Expression ◽  
Face Recognition System

Abstract: The LBPH algorithm is used ubiquitously for Face Recognition applications in modern times because of its simplicity of implementation, despite providing high accuracy and less computation time. However, in conditions of varied illumination, face expression and angles at which face images are captured, its confidence is decreased. We propose a slightly modified algorithm that considers the median of the neighbourhood pixels rather than the pixel itself to overcome this issue. This algorithm is called Median-LBPH. The grey value of every pixel is replaced by the median of all the neighbourhood pixel values. Then the features are extracted, and a histogram representing the original image is saved in the model. This model, in turn, can be used to compare with histograms obtained from the faces in real-time footage to find a potential match. This algorithm is used in an end-to-end face recognition system, a web application prototype for Law Enforcement Agencies to maintain a central criminal database shared and accessed across various departments. A live surveillance system is added as part of this novel application so that whenever an already registered criminal appears live on surveillance cameras, a notification will be received, and personnel appropriate Law Enforcement authorities will receive e-mail and text messages through a secured channel. Keywords: Face Recognition, Median-Local Binary Pattern Histogram (MLBPH), Haar Cascade, Adaboost, Neighbourhood Median

Incremental and Decremental Exponential Discriminant Analysis for Face Recognition

2014 ◽  
Vol 4 (1) ◽  
pp. 40-55 ◽  
Author(s):  
Nitin Kumar ◽  
R.K. Agrawal ◽  
Ajay Jaiswal
Keyword(s):  
Face Recognition ◽  
Discriminant Analysis ◽  
Small Sample Size ◽  
Computation Time ◽  
Small Sample ◽  
Incremental Algorithm ◽  
Linear Discriminant ◽  
Incremental Formulation ◽  
Face Datasets ◽  
Batch Formulation

Linear Discriminant Analysis (LDA) is widely used for feature extraction in face recognition but suffers from small sample size (SSS) problem in its original formulation. Exponential discriminant analysis (EDA) is one of the variants of LDA suggested recently to overcome this problem. For many real time systems, it may not be feasible to have all the data samples in advance before the actual model is developed. The new data samples may appear in chunks at different points of time. In this paper, the authors propose incremental formulation of EDA to avoid learning from scratch. The proposed incremental algorithm takes less computation time and memory. Experiments are performed on three publicly available face datasets. Experimental results demonstrate the effectiveness of the proposed incremental formulation in comparison to its batch formulation in terms of computation time and memory requirement. Also, the proposed incremental algorithms (IEDA, DEDA) outperform incremental formulation of LDA in terms of classification accuracy.

Real-time ensemble based face recognition system for NAO humanoids using local binary pattern

2017 ◽  
Vol 92 (3) ◽  
pp. 467-475 ◽  
Author(s):  
Anastasia Bolotnikova ◽  
Hasan Demirel ◽  
Gholamreza Anbarjafari
Keyword(s):  
Face Recognition ◽  
Real Time ◽  
Local Binary Pattern ◽  
Recognition System ◽  
Face Recognition System
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