A Survey on Accessible Context-Aware Systems

Doush, Iyad Abu; Damaj, Issam; Al-Betar, Mohammed Azmi; Awadallah, Mohammed A.; Al-Khatib, Ra’ed M.; Alchalabi, Alaa Eddin; Bolaji, Asaju La’aro

doi:10.1007/978-3-030-16450-8_2

Cited by 11 publications

(8 citation statements)

References 64 publications

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“…With the advancement in face acknowledgment innovation, it is currently utilized for cross fringe security. Hong Kong-SAR fringe has the world's first drive-through face acknowledgment framework [27], [28], [29]. Shrewd Gate at Australia, US visit, and Japan visit programs additionally gather all guests' faces.…”

Section: A General Face Recognitionmentioning

confidence: 99%

Twins and Similar Faces Recognition Using Geometric and Photometric Features with Transfer Learning

Nahar¹,

Abul-Huda²,

bataineh³

et al. 2022

IJCDS

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Twins recognition and identification is one of the important challenges in the field of image processing. The strong similarity between identical twins makes it hard to distinguish the twin from his/her sibling. Similarities come from biometric, geometric, and photometric features. In biometric patterns, the fingerprints found to be identical in some cases, geometrically, the twins' faces rarely differ which confuses people. Photometric features are very close to each other even though they rarely success in twins' recognition. We tackle this challenge by a model for twin's face recognition (FR) where our solution is based on deep transfer learning in terms of residual neural networks including two VGG16 trained networks, which are considered to be one of the powerful and deeply learned neural networks. For comparison purposes, we check other approaches to solve the twins' problem including iris, fingerprints, and lip corners. The data used was collected from Google which is a challenge. Data contains 4-pairs of twins with the 17-different position for each one which produces 5×2×17 (170) different images. Collected images were used for comparisons between features. Results show that geometrical features gave 85% of success while photometric features gave 96%. By hybridizing geometrical and photometric features together, the results reach 98% of accuracy. Biometric measures, in this research, prove the superiority of deeply transferring learning over traditional methods. The newly achieved method could be replaced to assist authentication systems that fully depend on biometric features.

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Section: A General Face Recognitionmentioning

confidence: 99%

Twins and Similar Faces Recognition Using Geometric and Photometric Features with Transfer Learning

Nahar¹,

Abul-Huda²,

bataineh³

et al. 2022

IJCDS

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show abstract

“…Doush et al researched the adaptation of indoor navigation systems for visually impaired people in the context of the scenario [ 123 ]. The authors’ objective was to identify the limitations in the countless strategies for using sensory technologies and algorithms to minimize user interactions with the system and provide continuous services, and automated and intelligent recognition about monitoring the environment.…”

Section: Comparison Of Systems Discussedmentioning

confidence: 99%

A Review of Technologies and Techniques for Indoor Navigation Systems for the Visually Impaired

Simões

Machado

Sales

et al. 2020

Sensors

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Technologies and techniques of location and navigation are advancing, allowing greater precision in locating people in complex and challenging conditions. These advances have attracted growing interest from the scientific community in using indoor positioning systems (IPSs) with a higher degree of precision and fast delivery time, for groups of people such as the visually impaired, to some extent improving their quality of life. Much research brings together various works that deal with the physical and logical approaches of IPSs to give the reader a more general view of the models. These surveys, however, need to be continuously revisited to update the literature on the features described. This paper presents an expansion of the range of technologies and methodologies for assisting the visually impaired in previous works, providing readers and researchers with a more recent version of what was done and the advantages and disadvantages of each approach to guide reviews and discussions about these topics. Finally, we discuss a series of considerations and future trends for the construction of indoor navigation and location systems for the visually impaired.

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“…One way to improve the usability is to change the interaction between the drivers and smartphone, using an adaptive user interface. e adaptive user interfaces use a context-awareness approach and generate new interfaces according to the change in environment, user preferences, and device usage [9,10]. is approach will help drivers in the personalization of their smartphone user interfaces irrespective of their visual, physical, and cognitive limitations.…”

Section: Introductionmentioning

confidence: 99%

Towards the Design of Context-Aware Adaptive User Interfaces to Minimize Drivers’ Distractions

Khan

Khusro

2020

Mobile Information Systems

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The usage of a smartphone while driving is a pervasive problem and has been acknowledged as a significant source of road accidents and crashes. Several solutions have been developed to control and minimize risky driving behavior. However, these solutions were mainly designed from the perspective of normal users to be used in a nondriving scenario. In a driving scenario, any deviation from these assumptions (e.g., touching or taping interfaces and looking to visual items) could impact driving performance. In this research paper, we aimed to design and develop a context-aware adaptive user interface framework to minimize driver distraction. The proposed framework is implemented in Android platform, namely, “DriverSense,” which is capable of adapting smartphone user interfaces based on contextual factors including driver preferences, environmental factors, and device usage in real time using adaptation rules. The proposed solution is evaluated both in real time using AutoLog application and through an empirical study by collecting data from 93 drivers through a mixed-mode survey using a questionnaire. Results obtained from AutoLog dataset show that performing activities on smartphone native interfaces while driving leads to abrupt changes in speed and steering wheel angle. However, minimal variations have been observed while performing activities on DriverSense interfaces. The results obtained from the empirical study show that the data are found to be internally consistent with 0.7 Cronbach’s alpha value. Furthermore, an Iterated Principal Factor Analysis (IPFA) retained 60 of a total of 61 measurement items with lower uniqueness values. The findings show that the proposed solution has significantly minimized the driver distractions and has positive perceptions in terms of usefulness, attitude, learnability and understandability, and user satisfaction.

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A Survey on Accessible Context-Aware Systems

Cited by 11 publications

References 64 publications

Twins and Similar Faces Recognition Using Geometric and Photometric Features with Transfer Learning

Twins and Similar Faces Recognition Using Geometric and Photometric Features with Transfer Learning

A Review of Technologies and Techniques for Indoor Navigation Systems for the Visually Impaired

Towards the Design of Context-Aware Adaptive User Interfaces to Minimize Drivers’ Distractions

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