Deep learning in face recognition for attendance system: an exploratory study / Mochamad Azkal Azkiya Aziz, Shahrinaz Ismail and Noormadinah Allias

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. 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. This paper presents the results from this interview, which provide an insight based on real practices.