LBPH-based Enhanced Real-Time Face Recognition

Deeba, Farah; Memon, Hira; Dharejo, Fayaz Ali; Ahmed, Aftab; Ghaffar, Abddul

doi:10.14569/ijacsa.2019.0100535

Cited by 65 publications

(30 citation statements)

References 17 publications

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“…Concerning face recognition, LBPH outperformed eigenfaces using the proposed framework with the hardware and the control environment setting. The results show that LBPH is robust in greyscale and illumination in real time [15,18,22]. It is also observed that the eigenfaces have difficulties in recognizing faces in a situation where the position of the face is similar across images of different subjects.…”

Section: Figure 2 Mask Detection and Face Detectionmentioning

confidence: 84%

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

Suhaimin¹,

Hijazi

Kheau

et al. 2021

Bulletin EEI

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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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Section: Figure 2 Mask Detection and Face Detectionmentioning

confidence: 84%

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

Suhaimin¹,

Hijazi

Kheau

et al. 2021

Bulletin EEI

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“…Local Binary Pattern Histogtram (LBPH) adalah sebuah kombinasi algoritma antara LBP dengan Histogram of Oriented Gradients(HOG) [14]. LBPH merupakan penyempurnaan performa dari LBP dalam proses face recognition.…”

Section: Selain Itu Terdapat Beberapa Metode Lain Yang Saat Ini Sedang Berkembang Seperti Speeded Up Robust Feature (Surf) Histogram Of Ounclassified

Penerapan Face Recognition Pada Sistem Presensi

Ramdhon

Febriya

2021

JACOST

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The development of digital image technology is increasing nowadays. However, the use of image technology on surveillance cameras has not been optimally utilized. On the other hand, the various presence data monitoring systems that currently exist have their respective advantages and disadvantages, and need to be continuously developed so as to facilitate the data processing. The student attendance system at STT Bandung is basically good but it is still not optimal. The process of collecting student attendance data is still quite time-consuming and still allows human errors to occur in the data input process. Therefore, the author intends to help overcome this by utilizing face recognition technology in an integrated presence process. LBPH (Local Binary Pattern Histogram) is currently the best method in face recognition technology because the detection and recognition process is relatively fast and has the highest level of accuracy when compared to other methods. After testing the resilience of the system from the results of the prototyping that was built, the results obtained with a success rate of 86.85%.

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“…Many methods can be used to recognize the face from a digital image. Local Binary Patterns Histogram (LBPH) method is one of the most useful techniques in face recognition procedures [19]. It divides a picture into a number of small regions and extracts the features from each one.…”

Section: -3-human Face Detection and Recognition Techniquementioning

confidence: 99%

Detection and Recognition of Moving Objects for Physical Systems Protection in Nuclear Facilities

Gaber

Khalaf

Mahmoud

et al. 2021

Journal of Advanced Engineering Trends

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Recently, the nuclear applications and services are entering in several domains of our life. It is a mandatory direction to protect the physical systems against any malicious sabotage or threat. Human activity detection and recognition techniques have a significant role in the physical protection systems. It enhances the protection regime through anomalous activity detection and authorized human-computer interaction applications. This paper is aimed to demonstrate a real-time system for moving object\human detection and recognition in order to protect the physical systems. It integrates computer vision with Internet of Things (IoT) technologies for detecting and recognizing the moving humans with their occupation times at the camera field. The proposed system can introduce more reliable and active intrusion detection scenarios with simple and low cost implementation techniques. It provides high flexibility for securing access to the monitoring information with storing for the historian purposes. Through an internet environment, the system can monitor the sensitive areas through detecting the unknown faces and their occupation times via ThingSpeak channels. The monitoring information is tracked through web based dashboards and ThingView mobile application. The system can launch automatically the alert notifications through SMS, and e-mail messages. These notifications are received whenever detecting an intruder face and exceeding the occupation times of human motion. The results provide effective features for implementing real-time and low cost applications even with low resolution cameras.

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LBPH-based Enhanced Real-Time Face Recognition

Cited by 65 publications

References 17 publications

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

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

Penerapan Face Recognition Pada Sistem Presensi

Detection and Recognition of Moving Objects for Physical Systems Protection in Nuclear Facilities

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