Face Retrieval Based On Local Binary Pattern and Its Variants: A Comprehensive Study

Khoi, Phan; Thien, Lam Huu; Viet, Vo Hoai

doi:10.14569/ijacsa.2016.070632

Cited by 12 publications

(11 citation statements)

References 19 publications

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“…Before detailing the techniques used, it is necessary to make a brief description of the problems that must be faced and solved in order to perform the face recognition task correctly. For several security applications, as detailed in the works of [17][18][19][20][21][22], the characteristics that make a face recognition system useful are the following: its ability to work with both videos and images, to process in real time, to be robust in different lighting conditions, to be independent of the person (regardless of hair, ethnicity, or gender), and to be able to work with faces from different angles. Different types of sensors, including RGB, depth, EEG, thermal, and wearable inertial sensors, are used to obtain data.…”

Section: Essential Steps Of Face Recognition Systemsmentioning

confidence: 99%

“…Figure 3 illustrates the procedure of the LBP technique. Khoi et al [20] propose a fast face recognition system based on LBP, pyramid of local binary pattern (PLBP), and rotation invariant local binary pattern (RI-LBP). Xi et al [15] have introduced a new unsupervised deep learning-based technique, called local binary pattern network (LBPNet), to extract hierarchical representations of data.…”

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confidence: 99%

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Face Recognition Systems: A Survey

Kortli

Jridi²,

Falou³

et al. 2020

Sensors

388

146

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Over the past few decades, interest in theories and algorithms for face recognition has been growing rapidly. Video surveillance, criminal identification, building access control, and unmanned and autonomous vehicles are just a few examples of concrete applications that are gaining attraction among industries. Various techniques are being developed including local, holistic, and hybrid approaches, which provide a face image description using only a few face image features or the whole facial features. The main contribution of this survey is to review some well-known techniques for each approach and to give the taxonomy of their categories. In the paper, a detailed comparison between these techniques is exposed by listing the advantages and the disadvantages of their schemes in terms of robustness, accuracy, complexity, and discrimination. One interesting feature mentioned in the paper is about the database used for face recognition. An overview of the most commonly used databases, including those of supervised and unsupervised learning, is given. Numerical results of the most interesting techniques are given along with the context of experiments and challenges handled by these techniques. Finally, a solid discussion is given in the paper about future directions in terms of techniques to be used for face recognition.

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Section: Essential Steps Of Face Recognition Systemsmentioning

confidence: 99%

mentioning

confidence: 99%

Face Recognition Systems: A Survey

Kortli

Jridi²,

Falou³

et al. 2020

Sensors

388

146

View full text Add to dashboard Cite

show abstract

“…Naive Bayes classifier to recognize candidate faces was used to, achieve 96.14% accuracy with a throughput of 60 FPS. In [41], a face recognition system based on LBP and its variants (i.e. Rotation LBP (RILBP) and Pyramid of LBP (PLBP)) were also implemented.…”

Section: Face Detection/recognition Algorithms Targeting Real-time An...mentioning

confidence: 99%

Hardware Accelerators for Real-Time Face Recognition: A Survey

2022

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Real-time face recognition has been of great interest in the last decade due to its wide and variant critical applications which include biometrics, security in public places, and identification in login systems. This has encouraged researchers to design fast and accurate embedded and portable systems that are capable to detect and recognize a large number of faces at almost video frame rate. Due to the increasing volume of reference faces, traditional general purpose computing engines such as the ones based on Intel's Pentium processors have shown not to be adequate and various dedicated hardware accelerators based on either Graphical Processing Unit (GPU), Field Programmable Gate Arrays (FPGA), Application Specific Integrated Circuit (ASIC), or even multi-core Central Processing Units (CPU) have emerged. Earlier published review papers on face detection/recognition have discussed face detection and face recognition algorithms enhancement that improve the detection accuracy. Nevertheless, none of them has reviewed the hardware accelerators used for this application. Accordingly, this paper aims to provide a comprehensive review of the most recent face recognition algorithms and associated embedded hardware systems targeting real-time performance. A detailed comparison between neural network and non-neural network-based algorithms in terms of accuracy and processing time is provided. Discussions on their suitability to be implemented into parallel hardware architectures such as Single Instruction Multiple Thread (SIMT) or Single Instruction Multiple Data (SIMD) is also discussed.

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“…These algorithms were chosen as they require less processing time for implementation. Furthermore, both have been shown to be feasible for implementation in a real-time environment and produced good recognition results [14][15][16].…”

Section: Face Recognitionmentioning

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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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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Face Retrieval Based On Local Binary Pattern and Its Variants: A Comprehensive Study

Cited by 12 publications

References 19 publications

Face Recognition Systems: A Survey

Face Recognition Systems: A Survey

Hardware Accelerators for Real-Time Face Recognition: A Survey

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

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