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This research aims to build a system that will continuously. This paper is an extended version of SECPRE 2021 paper and presents a research on the development and validation of a behavioral biometrics continuous authentication (BBCA) system that is based on users keystroke dynamics and touch gestures on mobile devices. This paper aims to build a system that will continuously authenticate the user of a smartphone.

Session authentication schemes establish the identity of the user only at the beginning of the session, so they are vulnerable to attacks that tamper with communications after the establishment of the authenticated session. Moreover, smartphones themselves are used as authentication means, especially in two-factor authentication schemes, which are often required by several services. Whether the smartphone is in the hands of the legitimate user constitutes a great concern and correspondingly whether the legitimate user is the one who uses the services. In response to these concerns, BBCA technologies have been proposed on a large corpus of literature. This paper presents a research on the development and validation of a BBCA system (named BioPrivacy), which is based on the user’s keystroke dynamics and touch gestures, using a multi-layer perceptron (MLP). Also, this paper introduces a new BB collection tool and proposes a methodology for the selection of an appropriate set of BB.

The system achieved the best results for keystroke dynamics which are 97.18% accuracy, 0.02% equal error rate, 97.2% true acceptance rate and 0.02% false acceptance rate.

This paper develops a new BB collection tool, named BioPrivacy, by which behavioral data of users on mobile devices can be collected. This paper proposes a methodology for the selection of an appropriate set of BB. This paper presents the development of a BBCA system based on MLP.

The purpose of this paper is to present a new paradigm, named BioGames, for the extraction of behavioral biometrics (BB) conveniently and entertainingly. To apply the BioGames paradigm, the authors developed a BB collection tool for mobile devices named BioGames App. The BioGames App collects keystroke dynamics, touch gestures, and motion modalities and is available on GitHub. Interested researchers and practitioners may use it to create their datasets for research purposes.

One major challenge for BB and continuous authentication (CA) research is the lack of actual BB datasets for research purposes. The compilation and refinement of an appropriate set of BB data constitute a challenge and an open problem. The issue is aggravated by the fact that most users are reluctant to participate in long demanding procedures entailed in the collection of research biometric data. As a result, they do not complete the data collection procedure, or they do not complete it correctly. Therefore, the authors propose a new paradigm and introduce a BB collection tool, which they call BioGames, for the extraction of biometric features in a convenient way. The BioGames paradigm proposes a methodology where users play games without participating in an experimental painstaking process. The BioGames App collects keystroke dynamics, touch gestures, and motion modalities.

The authors proposed a new paradigm for the collection of BB on mobile devices and created the BioGames application. The BioGames App is an Android application that collects BB data on mobile devices and sends them to a database. The database design allows multiple users to store their sensor data at any time. Thus, there is no concern about data separation and synchronization. BioGames App is General Data Protection Regulation (GDPR) compliant as it collects and processes only anonymous data.

The BioGames App is a publicly available tool that combines the keystroke dynamics, touch gestures, and motion modalities. In addition, it uses a methodology where users play games without participating in an experimental painstaking process.

For the success of future investments in the implementation of continuous authentication systems, we should explore the key factors that influence technology adoption. The authors investigate the effect of various factors of behavioral intention through the new incorporation of a modified technology acceptance model (TAM) and diffusion of innovation theory (DOI). Also, the authors have created a new theoretical framework with constructs such as security and privacy risks (SPR), biometrics privacy concerns (BPC) and perceived risk of using the technology (PROU). In this paper, the authors conducted a structural equation modeling empirical research. This research is designed in such a way to respond to the trade-off between users’ concern for the protection of their biometrics privacy and their protection from risks.

The authors provide an extensive conceptual framework for both existing models (TAM and DOI) and the new constructs that the authors have added to the model. In addition, this research explores external factors, such as trust in technology (TT) and innovativeness (Innov). In addition, the authors have introduced significant constructs, to overcome the limitations of the TAM and to adapt it to the needs of the present research. The new theoretical framework the authors introduce in the present research concerns the constructs SPR, BPC and PROU.

The authors found that the main facilitators of behavioral intention to adopt the technology (BI) are TT, followed by compatibility (COMP), perceived usefulness (PU) and Innov. This research also shows that individuals are less interested in the ease of use of the technology and are willing to sacrifice it to achieve greater security. COMP and Innov also play a significant role. Individuals who believe that the usage of the behavioral biometrics continuous authentication (BBCA) technology would fit into their lifestyle and would like to experiment with new technologies have a positive intention to adopt the BBCA technology. The new constructs the authors have added are SPR, BPC and PROU. The authors’ results support the hypotheses that SPR is a facilitator to PU and PU acts as a facilitator to BI. Consequently, the hypothesis that individuals do not feel adequately protected by classical methods will consider the usefulness of the BBCA as a technology for their extra protection against risks is confirmed by the model. Also, with the constructs BPC and PROU, the authors examined if individuals’ concerns regarding their biometrics privacy act as inhibitors in the BI. The authors concluded that individuals consider that the benefits of using BBCA technology are much more important than the risks for their biometrics privacy since the hypothesis that the major inhibitor of BI is PROU is not supported by the model.

To the best of the authors’ knowledge, this research is among the first in the field that examines the factors that influence the individuals’ decision to adopt BBCA technology.

Examines the thirteenth published year of the ITCRR. Runs the whole gamut of textile innovation, research and testing, some of which investigates hitherto untouched aspects. Subjects discussed include cotton fabric processing, asbestos substitutes, textile adjuncts to cardiovascular surgery, wet textile processes, hand evaluation, nanotechnology, thermoplastic composites, robotic ironing, protective clothing (agricultural and industrial), ecological aspects of fibre properties – to name but a few! There would appear to be no limit to the future potential for textile applications.

Examines the twelfth published year of the ITCRR. Runs the whole gamut of textile innovation, research and testing, some of which investigates hitherto untouched aspects. Subjects discussed include cotton fabric processing, asbestos substitutes, textile adjuncts to cardiovascular surgery, wet textile processes, hand evaluation, nanotechnology, thermoplastic composites, robotic ironing, protective clothing (agricultural and industrial), ecological aspects of fibre properties – to name but a few! There would appear to be no limit to the future potential for textile applications.

Examines the eleventh published year of the ITCRR. Runs the whole gamut of textile innovation, research and testing, some of which investigates hitherto untouched aspects. Subjects discussed include cotton fabric processing, asbestos substitutes, textile adjuncts to cardiovascular surgery, wet textile processes, hand evaluation, nanotechnology, thermoplastic composites, robotic ironing, protective clothing (agricultural and industrial), ecological aspects of fibre properties – to name but a few! There would appear to be no limit to the future potential for textile applications.

Examines the tenth published year of the ITCRR. Runs the whole gamut of textile innovation, research and testing, some of which investigates hitherto untouched aspects. Subjects discussed include cotton fabric processing, asbestos substitutes, textile adjuncts to cardiovascular surgery, wet textile processes, hand evaluation, nanotechnology, thermoplastic composites, robotic ironing, protective clothing (agricultural and industrial), ecological aspects of fibre properties – to name but a few! There would appear to be no limit to the future potential for textile applications.

The purpose of this paper is to provide a robust method for automatic detection of seam lines based only on digital images of the garments.

A local standard deviation pre‐processing filter is applied to enhance the contrast between the seam line and the texture and the Prewitt operator extracts the edges of the enhanced image. The seam line is detected by a maximum at the Radon transform. The proposed method is invariant to the illumination intensity and it has been also tested with moving average and fast Fourier transform low‐pass filters used in the pre‐processing module. Extensive experiments are carried out in the presence of additive Gaussian and uniform noise.

The proposed method detects 109 out of 118 seams when the local standard deviation is used at the pre‐processing stage, giving a mean distance error between the real and the estimated line of 2 mm when the image is digitised at 97 dpi. However, in case the images are distorted by additive Gaussian noise at 20 dB signal‐to‐noise ratio, the moving average low‐pass filtering method gives the best results, detecting 104 noisy images.

The proposed method detects seam lines that can be approximated by a continuation of straight lines. The current work can be extended in the detection of the curved parts of seam lines.

Since the method addresses garments instead of seam specimens, the proposed approach can be imported in automatic systems for online quality control of seams.

Local standard deviation belongs to first‐order statistics, which makes it suitable for texture analysis and that is why it is mostly used in web defect detection. The novelty in the approach, however, is that by considering the seam as an abnormality of the texture, the authors applied that method at the pre‐processing stage to enhance the seam before the detection. Moreover, the presented method is illumination invariant, a property that has not been addressed in similar methods.