A review of soft biometrics for IoT

Tomičić, Igor; Grd, Petra; Bača, Miroslav

doi:10.23919/mipro.2018.8400203

Cited by 7 publications

(5 citation statements)

References 19 publications

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“…The applications of standalone soft biometrics recognition or retrieval are experimented over the years in surveillance [164,180], social robotics [47], IoT [195], social media and mobile authentication [72,129]. These factors are strong motive to use soft biometrics for seamless authentication.…”

Section: Soft Biometrics: a Seamless Solution For Recognitionmentioning

confidence: 99%

Soft biometrics: a survey

Hassan

Izquierdo

Piatrik

2021

Multimed Tools Appl

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The field of biometrics research encompasses the need to associate an identity to an individual based on the persons physiological or behaviour traits. While the use of intrusive techniques such as retina scans and finger print identification has resulted in highly accurate systems, the scalability of such systems in real-world applications such as surveillance and border security has been limited. As a branch of biometrics research, the origin of soft biometrics could be traced back to need for non-intrusive solutions for extracting physiological traits of a person. Following high number of research outcomes reported in the literature on soft biometrics, this paper aims to consolidate the scope of soft biometrics research across four thematic schemes (i) a detailed review of soft biometrics research data sets, their annotation strategies and building a largest novel collection of soft traits; (ii) the assessment of metrics that affect the performance of soft biometrics system; (iii) a comparative analysis on feature and modality level fusion reported in the literature for enhancing the system performance; and (iv) a performance analysis of hybrid soft biometrics recognition system using multi-scale criterion. The paper also presents a detailed analysis on the global traits associated to person identity such as gender, age and ethnicity. The contribution of the paper is to provide a comprehensive review of scientific literature, identify open challenges and offer insights on new research directions in the filed.

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Section: Soft Biometrics: a Seamless Solution For Recognitionmentioning

confidence: 99%

Soft biometrics: a survey

Hassan

Izquierdo

Piatrik

2021

Multimed Tools Appl

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“…There are numerous application areas of IoT, which include, but are not limited to: smart cities, smart agriculture, smart power grids, smart health, smart transport, smart buildings, smart living solutions, smart human settlements and other [7]. In recent years, IoT applications have increased, especially in smart cities, smart homes, smart agriculture and smart healthcare [8].…”

Section: Introductionmentioning

confidence: 99%

Smart Internet of Things Modular Micro Grow Room Architecture

Tomičić¹,

Grd²,

Bernik³

2022

Interdisciplinary Description of Complex Systems

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This article proposes the Internet of Things-based self-sustaining modular grow room architecture for optimising the seed germination and seedling development process. The architecture is scalable and flexible as it can be adapted to particular environments, scopes, requirements and plant types; it is modular as the host room can contain one or more smaller-scale grow rooms, each of them controlling their own micro-environment independently. One of the main goals of the research was to develop such a system that could be deployed efficiently, with minimal costs and energy footprint, which would enable its practical usage primarily in private self-sustainable households. The usage of widely available and inexpensive components, open source code, and free cloud services all enabled us to reach such a goal. Besides simple automation mostly described by existing solutions, the architecture proposed within this article offers remote control and data processing and visualisation, data trend tracking, smart optimisation, and actuator control, and event notifications.

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“…Nowadays, IoT is becoming one of the most researched topics [1]. The IoT describes a future computing scenario in which everyday physical objects will be connected to the Internet and will be able to identify themselves with other devices [2].…”

Section: Introductionmentioning

confidence: 99%

“…Contributions: The proposed work's significant contributions are: (1) We tackled a challenging security threat issue: sensor tampering, where we can see if someone harms our devices physically. Then the administrator gets notified about the harmed device.…”

Section: Introductionmentioning

confidence: 99%

Anomaly Detection using Machine Learning to Discover Sensor Tampering in IoT Systems

Pathak

Saguna

Mitra

et al. 2021

ICC 2021 - IEEE International Conference on Communications

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With the rapid growth of the Internet of Things (IoT) applications in smart regions/cities, for example, smart healthcare, smart homes/offices, there is an increase in security threats and risks. The IoT devices solve real-world problems by providing real-time connections, data and information. Besides this, the attackers can tamper with sensors, add or remove them physically or remotely. In this study, we address the IoT security sensor tampering issue in an office environment. We collect data from real-life settings and apply machine learning to detect sensor tampering using two methods. First, a real-time view of the traffic patterns is considered to train our isolation forest-based unsupervised machine learning method for anomaly detection. Second, based on traffic patterns, labels are created, and the decision tree supervised method is used, within our novel Anomaly Detection using Machine Learning (AD-ML) system. The accuracy of the two proposed models is presented. We found 84% with silhouette metric accuracy of isolation forest. Moreover, the result based on 10 cross-validations for decision trees on the supervised machine learning model returned the highest classification accuracy of 91.62% with the lowest false positive rate.

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A review of soft biometrics for IoT

Cited by 7 publications

References 19 publications

Soft biometrics: a survey

Soft biometrics: a survey

Smart Internet of Things Modular Micro Grow Room Architecture

Anomaly Detection using Machine Learning to Discover Sensor Tampering in IoT Systems

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