A Novel Conceptual Model of e-Participation using Biometrics Technologies

Abstract

Participation from all relevant stakeholders is important to achieve the goal of every activity successfully. Nowadays, Information and Computer Technologies, for example, Biometrics, Internet of Things (IoT) and Big Data are used to support participation from the relevant stakeholders. Electronic Participation (e-Participation) already utilized broadly to empower people participation in politics, business, government, cultural activities. Moreover, Biometrics has been used broadly for an identification system. Biometrics system identifies physiological instead of behavioral attributes, such as palm veins, iris recognition, face recognition, fingerprint, DNA, palm print, hand geometry, retina, and odor/scent. Therefore, this research would like to collaborate e-Participation and Biometrics fields from the multidisciplinary perspective. Furthermore, the literature reviews show that research collaboration between e-Participation and Biometrics Technologies are still limited. Hence, the objective of this research was to develop a novel conceptual model of e-Participation using Biometrics technologies. This paper contributes by developing a novel conceptual model of biometrics technologies for e-Participation implementation. This research has some implications. For theory development, this research contributes to the novel conceptual model in e-Participation, E-Government, Information Systems, Informatics, Computer Science, Image Processing, and Biometrics fields. For Practice, the novel model could be utilized for practitioners, policy-makers, and other relevant stakeholders for e-Participation implementation using Biometrics Technologies.

A Bibliometric Analysis on E-Participation

“…Its implementation is not only a matter of technological ability or the participation process itself; instead, it is a combination of a complex factor that affects the participation process. Most of the previous frameworks only capture a specific domain without realising that it is connected to other factors (Yusuf et al, 2020).…”

Assessment of Non-Verbal Communication Online Job Recruitment Using Gray Level Co-Occurrence Matrix and Fuzzy C-Means Algorithm

“…The data grouping in the FCM is determined based on the degree of membership which has a value between 0 and 1. A co-occurrence matrix is a matrix that is defined over an image to be the distribution of co-occurring value at a given offset [3]. The GLCM is a classic method of texture feature extraction, which is effective in image recognition, image segmentation, image retrieval, image classification, and texture analysis methods [4].…”