Insight on face liveness detection: A systematic literature review

Raheem, Enas A.; Ahmad, Sharifah Mumtazah Syed; Adnan, Wan Azizun Wan

doi:10.11591/ijece.v9i6.pp5865-5175

Cited by 10 publications

(9 citation statements)

References 43 publications

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“…The accuracy of that system was 96.2%. Raheem et al [12] proposed many stages that used several methods such as agglomerative hierarchical clustering and LBP. In an image that contained multiple faces, they used content based image retrieval (CBIR), which is characterized by the same image property.…”

Section: Review Of Existing Methodsmentioning

confidence: 99%

Technique for recognizing faces using a hybrid of moments and a local binary pattern histogram

Ogla

Saeid

Shaker

2022

IJECE

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<p><span>The face recognition process is widely studied, and the researchers made great achievements, but there are still many challenges facing the applications of face detection and recognition systems. This research contributes to overcoming some of those challenges and reducing the gap in the previous systems for identifying and recognizing faces of individuals in images. The research deals with increasing the precision of recognition using a hybrid method of moments and local binary patterns (LBP). The moment technique computed several critical parameters. Those parameters were used as descriptors and classifiers to recognize faces in images. The LBP technique has three phases: representation of a face, feature extraction, and classification. The face in the image was subdivided into variable-size blocks to compute their histograms and discover their features. Fidelity criteria were used to estimate and evaluate the findings. The proposed technique used the standard Olivetti Research Laboratory dataset in the proposed system training and recognition phases. The research experiments showed that adopting a hybrid technique (moments and LBP) recognized the faces in images and provide a suitable representation for identifying those faces. The proposed technique increases accuracy, robustness, and efficiency. The results show enhancement in recognition precision by 3% to reach 98.78%.</span></p>

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Section: Review Of Existing Methodsmentioning

confidence: 99%

Technique for recognizing faces using a hybrid of moments and a local binary pattern histogram

Ogla

Saeid

Shaker

2022

IJECE

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“…The third step is the eligibility process. In this study, all of the articles that were found were checked manually to make sure that after the screening process, all of the remaining articles met the criteria [41]. The procedure was completed by reading the titles and abstracts of the selected papers.…”

Section: Eligibilitymentioning

confidence: 99%

Systematic literature review: application of deep learning processing technique for fig fruit detection and counting

et al. 2023

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Deep learning has shown much promise in target identification in recent years, and it's becoming more popular in agriculture, where fig fruit detection and counting have become important. In this study, a systematic literature review (SLR) is utilised to evaluate a deep learning algorithm for detecting and counting fig fruits. The SLR is based on the widely used 'Reporting Standards for Systematic Evidence Synthetics' (ROSES) review process. The study starts by formulating the research questions, and the proposed SLR approach is critically discussed until the data abstraction and analysis process is completed. Following that, 33 relevant research involving the agriculture sector, fruit, were selected from many studies. IEEE, Scopus, and Web of Sciences are three databases to investigate. Due to the lack of fig fruit research, fruit and vegetable studies have been included because they use similar methods and processes. The SLR found that various deep learning algorithms can count fig fruit in the field. Furthermore, as most approaches obtained acceptable results, deep learning's performance is acceptable in F1-score and average precision (AP), higher than 80%. Moreover, improvements can be produced by enhancing the existing deep learning model with the personal dataset.

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“…"There are very scarce systematic literature reviews (SLR)" obtainable in the ILD research area to the best of our knowledge. The study [17] is one of the current and important SLR providing a good outline of liveness detection techniques to face antispoofing. Furthermore, the study enhances value to the literature by contributing valued perceptions into various liveness detection techniques and face anti-spoofing techniques.…”

Section: Prior Researchmentioning

confidence: 99%

Iris Liveness Detection for Biometric Authentication: A Systematic Literature Review and Future Directions

et al. 2021

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Biometrics is progressively becoming vital due to vulnerabilities of traditional security systems leading to frequent security breaches. Biometrics is an automated device that studies human beings’ physiological and behavioral features for their unique classification. Iris-based authentication offers stronger, unique, and contactless identification of the user. Iris liveness detection (ILD) confronts challenges such as spoofing attacks with contact lenses, replayed video, and print attacks, etc. Many researchers focus on ILD to guard the biometric system from attack. Hence, it is vital to study the prevailing research explicitly associated with the ILD to address how developing technologies can offer resolutions to lessen the evolving threats. An exhaustive survey of papers on the biometric ILD was performed by searching the most applicable digital libraries. Papers were filtered based on the predefined inclusion and exclusion criteria. Thematic analysis was performed for scrutinizing the data extracted from the selected papers. The exhaustive review now outlines the different feature extraction techniques, classifiers, datasets and presents their critical evaluation. Importantly, the study also discusses the projects, research works for detecting the iris spoofing attacks. The work then realizes in the discovery of the research gaps and challenges in the field of ILD. Many works were restricted to handcrafted methods of feature extraction, which are confronted with bigger feature sizes. The study discloses that dep learning based automated ILD techniques shows higher potential than machine learning techniques. Acquiring an ILD dataset that addresses all the common Iris spoofing attacks is also a need of the time. The survey, thus, opens practical challenges in the field of ILD from data collection to liveness detection and encourage future research.

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Insight on face liveness detection: A systematic literature review

Cited by 10 publications

References 43 publications

Technique for recognizing faces using a hybrid of moments and a local binary pattern histogram

Technique for recognizing faces using a hybrid of moments and a local binary pattern histogram

Systematic literature review: application of deep learning processing technique for fig fruit detection and counting

Iris Liveness Detection for Biometric Authentication: A Systematic Literature Review and Future Directions

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