Automatic Ethnicity Classification from Middle Part of the Face Using Convolutional Neural Networks

This paper provides a follow-up audit of security checkpoints (or simply checkpoints) for mass transportation hubs such as airports and seaports aiming at the post-pandemic R&D adjustments. The goal of our study is to determine biometric-enabled resources of checkpoints for a counter-epidemic response. To achieve the follow-up audit goals, we embedded the checkpoint into the Emergency Management Cycle (EMC) -the core of any doctrine that challenges disaster. This embedding helps to identify the technologysocietal gaps between contemporary and post-pandemic checkpoints. Our study advocates a conceptual exploration of the problem using EMC profiling and formulates new tasks for checkpoints based on the COVID-19 pandemic lessons learned. In order to increase practical value, we chose a case study of face biometrics for an experimental post-pandemic follow-up audit. INDEX TERMSSecurity checkpoints, border control, mass-migration, follow-up audits, biometrics, counter-epidemic measures, post-pandemic, emergency management cycle, self-sovereign identity. NOMENCLATURE EMC Emergency Management Cycle. JHA Justice and Home Affairs. IR Infrared. R&D Research and Development. SSI Self-Sovereign Identity. Biosecurity Measures to mitigate the risks. of infectious diseases. Distributed ledger A database spreading across multiple sites or institutions. Vaccination passport A personal digital counter-epidemic certificate. Periocular biometric Area of a face around the eyes. The associate editor coordinating the review of this manuscript and approving it for publication was Vincenzo Conti .

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“…• Personal data including biometric traits, as well as automated gender, age, and ethnicity identification [51], [129];…”

Section: C: Trend 3 Human-machine Interactabilitymentioning

confidence: 99%

Post-Pandemic Follow-Up Audit of Security Checkpoints

et al. 2023

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“…A person's identification is the primary focus in face biometrics, but other soft biometric information, such as age, gender, ethnicity, or emotional state, is also significant [3]. Ethnicity categorization is a growing field of study and has a wide range of applications.…”

Section: Related Work 21 Backgroundmentioning

confidence: 99%

“…The face is the part of the human body that contains the most semantic information about an individual. Convolutional neural networks (CNNs) are increasingly being used to solve problems such as face recognition [2] and verification [3], gender recognition [4], aging prediction [5], and facial emotion recognition [6]. Ethnicity recognition, which is the ability of a system to discern whether an individual belongs to one of the ethnic groups based on facial appearance observations, has not received the same attention from the scientific community.…”

Section: Introductionmentioning

confidence: 99%

“…In addition to the difficulty of identifying universal distinguishing characteristics, ethnicity cannot be quantitatively measured, unlike other biometric factors such as gender. In the absence of genetic traits that can categorize individuals based on commonly recognized "ethnicities", the term "ethnicity" lacks biological meaning [2,3]. Instead, human-perceived distinctions in somatic face features are considered for categorization purposes.…”

Section: Introductionmentioning

confidence: 99%

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Classification of Ethnicity Using Efficient CNN Models on MORPH and FERET Datasets Based on Face Biometrics

Abdulwahid

2023

Applied Sciences

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Ethnic conflicts frequently lead to violations of human rights, such as genocide and crimes against humanity, as well as economic collapse, governmental failure, environmental problems, and massive influxes of refugees. Many innocent people suffer as a result of violent ethnic conflict. People’s ethnicity can pose a threat to their safety. There have been many studies on the topic of how to categorize people by race. Until recently, the majority of the work on face biometrics had been conducted on the problem of person recognition from a photograph. However, other softer biometrics such as a person’s age, gender, race, or emotional state are also crucial. The subject of ethnic classification has many potential uses and is developing rapidly. This study summarizes recent advances in ethnicity categorization by utilizing efficient models of convolutional neural networks (CNNs) and focusing on the central portion of the face alone. This article contrasts the results of two distinct CNN models. To put the suggested models through their paces, the study employed holdout testing on the MORPH and FERET datasets. It is essential to remember that this study’s results were generated by focusing on the face’s central region alone, which saved both time and effort. Classification into four classes was achieved with an accuracy of 85% using Model A and 86% using Model B. Consequently, classifying people according to their ethnicity as a fundamental part of the video surveillance systems used at checkpoints is an excellent concept. This categorization statement may also be helpful for picture-search queries.

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“…Recently, deep learning (DL) method makes an effort to automatically learn good representations from raw data with multi-layers stacked on each other, has grabbed substantial interest in research community because of its different applications in natural language processing, speech processing, and computer vision (CV), [1]. One type of DL is the convolu-In [11], the authors proposed a new classification algorithm based on machine learning mechanism.…”

Section: Introductionmentioning

confidence: 99%

Automated ethnicity recognition using equilibrium optimizer with machine learning on facial images

et al. 2022

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In recent times, computer vision related face image analysis has gained significant attention in various applications namely biometrics, surveillance, security, data retrieval, informatics, etc. The main objective of the facial analysis is to extract facial soft biometrics like expression, identity, age, ethnicity, gender, etc. Of these, ethnicity recognition is considered a hot search topic, a major part of community with deep connections to many social and ecological concerns. The deep learning and machine learning methods is merit for effective ethnicity classification and recognition. This study develops a facial imaging based ethnicity recognition using equilibrium optimizer with machine learning (FIER-EOML) model. The goal of the FIER-EOML technique is to detect and classify different kinds of ethnicities on facial images. To accomplish this, the presented FIER-EOML technique applies an EfficientNet model to generate a set of feature vectors. For ethnicity recognition, the presented model uses long short-term memory method. To improve the recognition performance, the FIER-EOML technique utilizes EO algorithm for hyperparameter tuning process. The performance validation of the FIER-EOML technique is tested on BUPT-GLOBALFACE dataset and the results are examined under several measures. The comprehensive comparison study reported the enhanced performance of the FIER-EOML technique over other recent approaches.

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Automatic Ethnicity Classification from Middle Part of the Face Using Convolutional Neural Networks

Cited by 11 publications

References 43 publications

Post-Pandemic Follow-Up Audit of Security Checkpoints

Post-Pandemic Follow-Up Audit of Security Checkpoints

Classification of Ethnicity Using Efficient CNN Models on MORPH and FERET Datasets Based on Face Biometrics

Automated ethnicity recognition using equilibrium optimizer with machine learning on facial images

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