Face Recognition for Automatic Border Control: A Systematic Literature Review

Abstract

Context: Facial recognition is one aspect of research that still has broad potential for research and development, especially as a security system for automatic border control. There is a significant continuous need to understand the characteristics of system development by considering system complexity and implementation environmental conditions. Objective: This research aims to provide in-depth insight and assist researchers and practitioners in developing large-scale facial detection systems for automatic border control. It has a high level of complexity that necessitates special attention to several factors such as real-time system, privacy, variations in facial features, quantity of data, model, and implementation environment. Method: This study used a systematic literature review as a research methodology by Kitchenham. The analysis was based on studies published between 2019 and 2023 on using facial recognition in autonomous border control. A systematic analysis of research was conducted by examining 112 scientific studies from 7884 papers in scientific databases. Result: Based on research questions, 12 types of threats are often encountered in ABC face recognition, which can be seen in section IV. The method most widely used is deep learning, especially for detecting emotional features and morphing attacks. Apart from that, most datasets used are private because they require collaboration with organizations and are related to privacy. Three remaining issues are encountered in this research, including face recognition methodology, privacy, and architecture for large-scale development. Future directions: This study suggests two future research topics to enhance achieving desired results in large-scale and complex advancements in a methodical and structured while upholding privacy ethics.

Keywords

• Automatic border control
• big data
• face recognition
• large-scale facial detection