Deep learning techniques for biometric security: A systematic review of presentation attack detection systems

Biometric technology, including finger vein, fingerprint, iris, and face recognition, is widely used to enhance security in various devices. In the past decade, significant progress has been made in improving biometric systems, thanks to advancements in deep convolutional neural networks (DCNN) and computer vision (CV), along with large-scale training datasets. However, these systems have become targets of various attacks, with presentation attacks (PAs) being prevalent and easily executed. PAs involve displaying videos, images, or full-face masks to trick biometric systems and gain unauthorized access. Many authors are currently focusing on detecting these presentation attacks (PAD) and have developed several methods, particularly those based on deep learning (DL), which have shown superior performance compared to other techniques. This survey article focuses on manuscripts related to deep learning presentation attack detection, spoof attack detection using deep learning, and anti-spoofing deep learning methods for biometric finger vein, fingerprint, iris, and face recognition. The studies were primarily sourced from four digital research libraries: ACM, Science Direct, Springer, and IEEE Xplore. The article presents a comprehensive review of DL-based PAD systems, examining recent literature on DL-based PAD methods in finger vein, fingerprint, iris, and face detection systems. Through extensive research of the literature, recent algorithms and their solutions for relevant PAD approaches are thoroughly analyzed. Additionally, the article provides a performance analysis and highlights the most promising research findings. The discussion section addresses current issues, opportunities for advancement, and potential solutions associated with deep learning-based PAD methods. This study is valuable to various community users seeking to understand the significance of this technology and its recent applicability in the development of biometric technology for deep learning.