A Preliminary Study of Cashless Payment Face Recognition System Development in Malaysia

Ismail, Sarerusaenye; Ismail, S.H. Nurhasan

doi:10.1109/imcom53663.2022.9721723

Cited by 2 publications

(1 citation statement)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thus, the combination of algorithms can be used to obtain certain results. The respondent added that according to her article, in this case, deep learning algorithms are precisely to be used to enhance parameters optimization (Ismail & Ismail, 2022).…”

Section: Deep Learning Algorithm In Face Recognitionmentioning

confidence: 99%

Deep Learning in Face Recognition for Attendance System: An Exploratory Study

Aziz

Ismail

Allias

2022

JCRINN

View full text Add to dashboard Cite

Conventional-manual type of attendance systems can be very time-consuming to some extent, particularly for a significant number. The existence of face recognition technology can solve the inefficiency and ineffectiveness of conventional and manual attendance systems. Among many approaches to implement face recognition, this research focuses on using deep learning approaches as it has been proven to give promising results. There are various algorithms for face recognition, such as Local Binary Pattern Histogram (LBPH), Local Binary Pattern Network (LBPn), Haar Cascade, and Convolutional Neural Network. The use of deep learning can reach 98 percent accuracy. However, it is necessary to conduct further research on its implementation on the real system in order to evaluate the efficiency of the system.Â An interview was conducted with an expert in the field, to understand the concept, trend, and use of deep learning in face recognition, as well as to determine the suitable algorithm for the attendance system.Â This paper presents the results from this interview, which provide an insight based on real practices.

show abstract

Section: Deep Learning Algorithm In Face Recognitionmentioning

confidence: 99%