scholarly journals Sistem Pengenalan Wajah dengan Algoritma Haar Cascade dan Local Binary Pattern Histogram

2018 ◽  
Vol 14 (1) ◽  
pp. 62-67 ◽  
Author(s):  
Sayeed Al-Aidid ◽  
Daniel Pamungkas
Keyword(s):  
Local Binary Pattern ◽  
Haar Cascade ◽  
Pattern Histogram

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 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 Unsupervised Local Binary Pattern Histogram Selection Scores for Color Texture Classification

2018 ◽  
Vol 4 (10) ◽  
pp. 112 ◽  
Author(s):  
Mariam Kalakech ◽  
Alice Porebski ◽  
Nicolas Vandenbroucke ◽  
Denis Hamad
Keyword(s):  
Local Binary Pattern ◽  
Color Image ◽  
Texture Classification ◽  
Classification Problems ◽  
Class Label ◽  
Laplacian Score ◽  
Pattern Histogram ◽  
Color Texture

These last few years, several supervised scores have been proposed in the literature to select histograms. Applied to color texture classification problems, these scores have improved the accuracy by selecting the most discriminant histograms among a set of available ones computed from a color image. In this paper, two new scores are proposed to select histograms: The adapted Variance score and the adapted Laplacian score. These new scores are computed without considering the class label of the images, contrary to what is done until now. Experiments, achieved on OuTex, USPTex, and BarkTex sets, show that these unsupervised scores give as good results as the supervised ones for LBP histogram selection.

Implementasi Webcam sebagai Pendeteksi Wajah pada Sistem Keamanan Perumahan menggunakan Image Processing

ELECTRICES ◽  
2020 ◽  
Vol 2 (1) ◽  
pp. 1-5
Author(s):  
Arba Abdul Syukur
Keyword(s):  
Image Processing ◽  
Local Binary Pattern ◽  
Raspberry Pi ◽  
Pattern Histogram ◽  
False Reject Rate

Pencurian yang sangat meresahkan masyarakat seringkali terjadi pada suatu ruangan atau lingkungan seperti gedung, kantor, lorong bahkan tempat ibadah juga menjadi sasaran para pencuri. Upaya yang dilakukan DKM (Dewan Kemakmuran Masjid) yaitu memberikan himbauan supaya tetap menjaga barang pentingnya masing-masing.  Masjid seharusnya menjadi tempat yang aman dan nyaman untuk dikunjungi. Oleh karena itu kami memiliki ide yang bertujuan untuk mengantisipasi pencurian di masjid atau tempattempat yang rawan pencurian. Penelitian ini merancangbangun sistem pengenalan wajah sebagai solusi untuk mengurangi tingkat pencurian. Sistem ini dilengkapi dengan perangkat keras Raspberry Pi 3 model B dan webcam A4Tech. Perangkat lunak database yang dapat menyimpan data pengguna. Tujuan penelitian untuk membandingkan 2 metode yang terbaik dalam pengenalan wajah yaitu metode LBPH (Local Binary Pattern Histogram) dan metode Eigenface. Penelitian dilakukan pada siang hari untuk mengambil citra wajah yang berbeda. Penelitian dilakukan dengan 3 kondisi yaitu siang hari luar ruangan, siang hari dalam ruangan dan malam hari dalam ruangan. Parameter yang digunakan untuk melihat hasil dari pengenalan wajah yaitu Akurasi, FAR (False Accept Rate) dan FRR (False Reject Rate). Hasil pengujian 2 metode tersebut yang memiliki tingkat rata-rata Akurasi tertinggi dan tingkat rata-rata FAR dan FRR terendah adalah metode  Eigenface. Kesimpulan dari hasil penelitian yaitu pencahayaan mempengaruhi pengenalan wajah dalam 2 metode tersebut.

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.

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