Time evolution of face recognition in accessible scenarios

Blanco-Gonzalo, Ramón; Poh, Norman; Wong, Rita; Sánchez-Reillo, Raúl

doi:10.1186/s13673-015-0043-0

Cited by 16 publications

(7 citation statements)

References 28 publications

(19 reference statements)

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“…Further experiments have analysed the convenience of using specific modalities for elderly [ 2 ], [ 10 ] and for people with visual impairments [ 11 ]–[ 13 ], concluding that biometric recognition could ease common tasks such as banking transactions or mobile authentication. In studies of biometrics with elderly users, outcomes suggest that users have initial anxieties due to distrust and fear of unknown technologies.…”

Section: State Of the Artmentioning

confidence: 99%

Biometrics: Accessibility challenge or opportunity?

et al. 2018

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Biometric recognition is currently implemented in several authentication contexts, most recently in mobile devices where it is expected to complement or even replace traditional authentication modalities such as PIN (Personal Identification Number) or passwords. The assumed convenience characteristics of biometrics are transparency, reliability and ease-of-use, however, the question of whether biometric recognition is as intuitive and straightforward to use is open to debate. Can biometric systems make some tasks easier for people with accessibility concerns? To investigate this question, an accessibility evaluation of a mobile app was conducted where test subjects withdraw money from a fictitious ATM (Automated Teller Machine) scenario. The biometric authentication mechanisms used include face, voice, and fingerprint. Furthermore, we employed traditional modalities of PIN and pattern in order to check if biometric recognition is indeed a real improvement. The trial test subjects within this work were people with real-life accessibility concerns. A group of people without accessibility concerns also participated, providing a baseline performance. Experimental results are presented concerning performance, HCI (Human-Computer Interaction) and accessibility, grouped according to category of accessibility concern. Our results reveal links between individual modalities and user category establishing guidelines for future accessible biometric products.

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Section: State Of the Artmentioning

confidence: 99%

Biometrics: Accessibility challenge or opportunity?

et al. 2018

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“…As noted previously, research has also shown that the physiology of a user can affect the performance of a biometric system, including age [21] and accessibility [34]. Another important factor when discussing how users affect system performance is to examine users' acceptance and satisfaction of biometric systems as this, in turn, will not only drive the overall performance but will indicate the willingness to use such a system.…”

Section: Factor #Iv: Usersmentioning

confidence: 99%

Exploring Mobile Biometric Performance Through Identification of Core Factors and Relationships

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et al. 2019

IEEE Trans. Biom. Behav. Identity Sci.

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“…Image plays an important role in numerous human activities such as law enforcement [12, 13], agriculture and forestry management [14, 15], earth science [16] and so forth. One of the uses of images is in face recognition and identification [17, 18]. …”

Section: Introductionmentioning

confidence: 99%

Classification of Suncus murinus species complex (Soricidae: Crocidurinae) in Peninsular Malaysia using image analysis and machine learning approaches

et al. 2016

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BackgroundTaxonomists frequently identify specimen from various populations based on the morphological characteristics and molecular data. This study looks into another invasive process in identification of house shrew (Suncus murinus) using image analysis and machine learning approaches. Thus, an automated identification system is developed to assist and simplify this task. In this study, seven descriptors namely area, convex area, major axis length, minor axis length, perimeter, equivalent diameter and extent which are based on the shape are used as features to represent digital image of skull that consists of dorsal, lateral and jaw views for each specimen. An Artificial Neural Network (ANN) is used as classifier to classify the skulls of S. murinus based on region (northern and southern populations of Peninsular Malaysia) and sex (adult male and female). Thus, specimen classification using Training data set and identification using Testing data set were performed through two stages of ANNs.ResultsAt present, the classifier used has achieved an accuracy of 100% based on skulls’ views. Classification and identification to regions and sexes have also attained 72.5%, 87.5% and 80.0% of accuracy for dorsal, lateral, and jaw views, respectively. This results show that the shape characteristic features used are substantial because they can differentiate the specimens based on regions and sexes up to the accuracy of 80% and above. Finally, an application was developed and can be used for the scientific community.ConclusionsThis automated system demonstrates the practicability of using computer-assisted systems in providing interesting alternative approach for quick and easy identification of unknown species.

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Time evolution of face recognition in accessible scenarios

Cited by 16 publications

References 28 publications

Biometrics: Accessibility challenge or opportunity?

Biometrics: Accessibility challenge or opportunity?

Exploring Mobile Biometric Performance Through Identification of Core Factors and Relationships

Classification of Suncus murinus species complex (Soricidae: Crocidurinae) in Peninsular Malaysia using image analysis and machine learning approaches

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