Face recognition attendance system using Local Binary Pattern (LBP)

Elias, Shamsul Jamel; Hatim, Shahirah Mohamed; Hassan, Nur Anisah; Latif, Lily Marlia Abd; Ahmad, R. Badlishah; Darus, Mohamad Yusof; Shahuddin, Ahmad Zambri

doi:10.11591/eei.v8i1.1439

Cited by 35 publications

(18 citation statements)

References 11 publications

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“…The camera of a full HD with a 3.6mm lens was used with the support of LED ring light surrounding it. This is to ensure a better face image is captured [3] under control environment [20,21]. Section 4.1 presents the results of the mask detection and face recognition performance, and section 4.2 describes the implementation of the proposed face recognition system.…”

Section: Resultsmentioning

confidence: 99%

“…Several face recognition systems have been proposed for the attendance system using a holistic, geometric, local and deep learning approach [2]. Recently local-based approach using local binary pattern (LBP) has been adopted for attendance registration [3] and produced better image features for recognition [4]. The exploited features for LBP involved contrast adjustment, bilateral filter, histogram equalization and image blending to improve the accuracy of face recognition.…”

Section: Related Workmentioning

confidence: 99%

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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: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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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show abstract

“…Key frame-based saliency detection and real-time action with 3D deep learning are also part of HAR [41,42]. The arrangement of HAR is created as structures to enable the constant checking and examination of human practices in various zones, for example, clever human action and conduct investigation intelligent human activity and behavior analysis [33,[43][44][45][46], sports injury detection [47], patient rehabilitation [33], monitor activity shifts amid elderly citizens that might be helpful to detect and diagnose serious illness [48,49], monitoring children's surveillance, hospital/patient monitoring [50,51], recognition and classification of the human usual and unusual activities [26,[52][53][54][55][56], human behavior recognition and human activity detection [53,57,58], criminal tracking system [59,60], automatic attendance system [61,62], etc.…”

Section: Literature Reviewmentioning

confidence: 99%

Suspicious Activity Recognition Using Proposed Deep L4-Branched-Actionnet With Entropy Coded Ant Colony System Optimization

et al. 2021

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Intelligent visual surveillance systems are attracting much attention from research and industry. The invention of smart surveillance cameras with greater processing power has now been the leading stakeholder, making it conceivable to design intelligent visual surveillance systems. It is possible to assure the safety of people in both homes and public places. This work aims to distinguish the suspicious activities for surveillance environments. For this, a 63 layers deep CNN model is suggested and named "L4-Branched-ActionNet". The suggested CNN structure is centered on the alteration of AlexNet with added four blanched sub-structures. The developed framework is first transformed into a pre-trained framework by conducting its training on an object detection dataset called CIFAR-100 with the SoftMax function. The dataset for suspicious activity recognition is then forwarded to this pretrained model for feature acquisition. The acquired deep features are subjected to feature subset optimization. These extracted features are first coded by applying entropy and then an ant colony system (ACS) is utilized on the entropy-based coded features for optimization. The configured features are then fed into numerous SVM and KNN based classification models. The cubic SVM has the highest efficiency scores, with a performance of 0.9924 in terms of accuracy. The proposed model is also validated on the Weizmann action dataset and attained an accuracy of 0.9796. The successful findings indicate the suggested work's soundness.

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“…A conventional approach to record student attendance is performed by asking every student to sign on an attendance list that passes through all students during the beginning of lectures. With the growing number of Android smartphone, Sunaryono et al [12], Khan et al [13], Elias et al [14], and Shen et al [15] have developed a mobile-based attendance system using face recognition based approach. Face recognition can also be applied for computer security purpose by using an available face databases.…”

Section: Face Recognition As An Enablersmentioning

confidence: 99%

Face Recognition State-of-the-art, Enablers, Challenges and Solutions: A Review

Moung¹

2020

IJATCSE

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In the past decade, face recognition has gained an important role among the most frequently used image processing applications and the availability of viable technologies in this field has also contributed significantly to this. Face recognition has become an enabler in healthcare, surveillance, photo cataloging, attendance, and much more, which will be discussed in this review paper. Despite rapid progress in face-recognition technology, various challenges such as variations, occlusion, facial expressions, aging and many more that affect the performance of the system still need to be addressed. This paper presents a review on the state-of-the-art, enablers, challenges and solutions for face recognition. Face recognition can be categorized into three groups; namely global approach, local feature approach, and hybrid approach. The global approach uses the whole face as input for the face recognition system. The local approach uses measurements between important landmarks of a face and certain face region selection for training. The hybrid approach blends global and local approaches in which the hybrid approach uses the best global and local approach methods. The challenges of face recognition are; (i) automated face detection where difficulties lies on detecting a person's face, (ii) pose variations cause by rotation of people's head, (iii) face occlusion, (iv) facial expression changes, (v) ageing of the face, (vi) varying illumination conditions, (vii) low image resolution, (viii) identity look-alike or twin, and (ix) other technical difficulties. Finally, the solutions to each of the highlighted challenges were described. The survey found that all the images considered for training and testing were made up of RGB images. With the rapid growth of computer technology in terms of computing speed and the increasingly sophisticated functions of smartphones, multispectral or even hyperspectral imagery could be considered for face-recognition research.

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Face recognition attendance system using Local Binary Pattern (LBP)

Cited by 35 publications

References 11 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

Suspicious Activity Recognition Using Proposed Deep L4-Branched-Actionnet With Entropy Coded Ant Colony System Optimization

Face Recognition State-of-the-art, Enablers, Challenges and Solutions: A Review

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