Predictive Modeling of Trait-Aging Invariant Face Recognition System Using Machine Learning

Okokpujie, Kennedy; Apeh, Simon T.

doi:10.1007/978-981-15-1465-4_43

Cited by 9 publications

(9 citation statements)

References 17 publications

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“…Next, most papers also use small datasets, with the only exception being (Choi and Lee, 2020), where the authors use FERET. Some other notable trends that we noticed were that (Peng et al, 2016) and (Okokpujie and Apeh, 2020) target specific challenges in face recognition. NIRFaceNet is designed for near-infrared face identification, specifically in non-cooperative-user applications with blur and noise.…”

Section: 60%mentioning

confidence: 61%

“…One very important finding was that there were 17 papers (Zafar et al, 2019;Khalajzadeh et al, 2013;Syafeeza et al, 2015;Peng et al, 2016;Bussey et al, 2017;Zeng et al, 2017;Almabdy and Elrefaei, 2019;Zeng et al, 2018a;Khan et al, 2020;Okokpujie and Apeh, 2020;Choi and Lee, 2020;Sepas-Moghaddam et al, 2020;Kim et al, 2020;Suharjito and Puspita, 2020;Darma and Mohamad, 2021;Alhanaee et al, 2021;Li et al, 2022) that reported an accuracy od 100%. With 4 papers (Bussey et al, 2017;Almabdy and Elrefaei, 2019;Sepas-Moghaddam et al, 2020;Li et al, 2022) reporting an accuracy of 100% in more than one category.…”

Section: 60%mentioning

confidence: 99%

“…NIRFaceNet is designed for near-infrared face identification, specifically in non-cooperative-user applications with blur and noise. In contrast, (Okokpujie and Apeh, 2020) focuses on trait aging invariant face recognition, adapting a CNN model using transfer learning to develop an aging-invariant system. (Zeng et al, 2017) tackles the problem of recognizing faces with only a single sample per person.…”

Section: 60%mentioning

confidence: 99%

See 2 more Smart Citations

Convolutional Neural Networks for Face Recognition: A Systematic Literature Review

Barcic¹,

Grd²,

Tomičić³

2023

Preprint

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This paper presents a comprehensive overview of Convolutional Neural Networks (CNNs) in the context of face recognition. By analyzing 150 research papers, we investigate major publication channels, temporal trends, prevalent CNN models, primary data sets, accuracy levels, primary research focuses, and future prospects for improving CNN-based facial recognition. A major focus is placed on identifying prevalent CNN architectures, techniques used for facial recognition and shedding light on the evolving landscape of CNN designs. Furthermore, we examine the datasets used for training and testing CNNs, and evaluate the accuracy levels achieved by these models. Lastly, we discuss future directions for enhancing CNN-based facial recognition, including addressing bias and fairness, improving robustness to environmental variations, privacy preservation, and exploring transfer learning and multimodal fusion. This paper serves as a valuable resource, summarizing major trends in CNN-based face recognition. It provides insights for researchers and practitioners, guiding future advancements in this rapidly evolving field.

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Section: 60%mentioning

confidence: 61%

Section: 60%mentioning

confidence: 99%

Section: 60%mentioning

confidence: 99%

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Convolutional Neural Networks for Face Recognition: A Systematic Literature Review

Barcic¹,

Grd²,

Tomičić³

2023

Preprint

View full text Add to dashboard Cite

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“…Increased image count in the dataset, less over-fitting in the algorithm, and improved feature extraction are all benefits of adding noise. Data augmentation is the process of adding noise to photos in order to boost the quantities [24]. The idea of data augmentation has a long history in literature and everyday life [25][26][27][28][29][30][31].…”

Section: Fg-net Dataset Pre-processingmentioning

confidence: 99%

Designing an Adaptive Age-Invariant Face Recognition System for Enhanced Security in Smart Urban Environments

Okokpujie,

Subair

et al. 2023

ISI

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The advent of smart technology in urban environments has often been hailed as the solution to a plethora of contemporary urban challenges, ranging from environmental conservation to waste management and transportation. However, the critical aspect of security, encompassing crime detection and prevention, is frequently overlooked. Moreover, there is a dearth of research exploring the potential disruption of conventional face detection and recognition systems by new smart city surveillance security cameras, particularly those which autonomously update their databases. This paper addresses this gap by proposing the enhancement of security in smart cities through the development of an adaptive Age-Invariant Face Recognition (AIFR) model. A non-intrusive AIFR model was constructed using a convolutional neural network and transfer learning techniques, and was then integrated into surveillance cameras. These cameras, designed to capture the faces of city residents at regular intervals, consequently updated their databases autonomously. Upon testing, the developed model demonstrated its potential to substantially improve security by effectively detecting and identifying the residents and visitors of smart cities, and updating their database profiles. Remarkably, the model retained its effectiveness even with significant age intra-class variation, with the capability to alert relevant authorities about potential criminals or missing individuals. This research underscores the potential of adaptive face recognition systems in bolstering security measures within smart urban environments.

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“…It secured electronic voting system that eliminated the possibility of impersonation by using two biometric traits and automating the voting process to save time. The resulting system had the propensity to improve the integrity factor of the voting process by making it fast, transparent and robust [11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

A secured automated bimodal biometric electronic voting system

Okokpujie

John

et al. 2021

IJ-AI

Self Cite

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Insecurity, rigging and violence continue to mar electoral processes in developing nations. It has been difficult to enforce security and transparency in the voting process. This paper proposes a secure and automated bimodal voting system. The system uses three security layers, namely, a unique ID code, a token passcode that expires every five minutes and biometrics (iris and fingerprint). A scanner captures the fingerprint and iris of eligible voters. The fingerprint and iris images stored along with the corresponding particulars in a database. The software implemented is a .net managed code in C#. The result of this system shows the system is transparent, fast and fraud-free. The proposed method had a failure to enroll (FTE) and a failure to capture (FTC) of zero.

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Predictive Modeling of Trait-Aging Invariant Face Recognition System Using Machine Learning

Cited by 9 publications

References 17 publications

Convolutional Neural Networks for Face Recognition: A Systematic Literature Review

Convolutional Neural Networks for Face Recognition: A Systematic Literature Review

Designing an Adaptive Age-Invariant Face Recognition System for Enhanced Security in Smart Urban Environments

A secured automated bimodal biometric electronic voting system

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