Near-infrared-visible, or NIR-VIS, face recognition technology has recently gained popularity because to its relative dependability in the face of numerous environmental issues, including abrupt changes in lighting, shadows, and position fluctuations. An extensive review of face recognition using the NIR-VIS technique is included in this article. This review covers the recent successes and issues with this specific approach, as well as possible avenues for further research. The main components of NIR-VIS face recognition platforms, including initial setups, qualifying characteristics, similarity matching, and assessment metrics, are examined in this paper. Furthermore, hybrid approaches that incorporate information from (NIR-VIS) spectra are given particular attention in order to improve the recognition algorithm's efficacy. In addition, the study presents performance criteria for system evaluation and benchmark datasets for assessing face recognition algorithms that exploit the NIR-VIS spectrum. In addition to discussing differences like domain coverage, data heterogeneity, and privacy issues, this article also identifies some potential remedies. Lastly, a critical review of new developments and recommendations for future study in NIR-VIS face identification is given. The purpose of this synopsis is to encourage scholars and professionals to investigate novel approaches that may enhance the efficiency and usefulness of these systems in actual situations.