Advancements in Multi-Modal Face Recognition: A State-of-the-Art Review of Security Biometrics

Abstract

This survey is a state-of-the-art review of recent advancements in face recognition, particularly multi-modal face recognition and its associated applications in the domain of security biometrics and identity verification. In this regard, the paper sheds light on the significance of the Sejong Face database and several other leading databases as equally significant tools in studying some of the most challenging tasks in face recognition: occluded and diverse face identification, cross-modality evaluation, and infrared-visible face detection. It also insists on the high challenge of face identification across scenarios and with different forms of disguise. In addition, it emphasizes the need for an effective security framework to be implemented with other major sectors that deal in finances. The purpose is to develop an exhaustive list of methodologies and innovations based on the Sejong Face Database with the intention of finding new methods and algorithms that guarantee accuracy and reliability. Specifically, the application of deep learning methods comprising convolutional neural networks with cross-modality discriminator networks and unit-class loss demonstrates significant advancements in the development of face recognition systems; consequently, these systems exhibit enhanced security and efficacy. The review article is an important source that points out the current status of the research, as well as future possibilities, regarding biometric authentication and its implications in the digital scenario.