Multi-modal face recognition technology can fully utilize face features and other biometric features to enhance recognition robustness and security， and has broad practical application value. Current research on multi-modal face recognition has problems such as modal disparity and inefficient modal fusion. Therefore， based on multiple information modalities and application purposes， the existing multi-modal face recognition methods were classified and reviewed to sort out the problems in research and explore future development directions. Firstly， the multi-modal face recognition research based on multi-source information fusion was divided into sensor-level， feature-level， scoring-level， and decision-level ones according to different stages of data processing， and advantages， limitations， and applicable scenarios of the existing methods were summarized. Secondly， the research on information-enhanced multi-modal face recognition was categorized into 2D-3D and 3D-2D information enhancement ones according to different enhanced modalities， and advantages and disadvantages of the existing methods were summed up. Thirdly， multi-modal face recognition methods based on other biometric features and for anti-spoofing were summarized， and the relevant information of commonly used multi-modal face recognition datasets were introduced briefly. Finally， key challenges and future development directions were given and prospected.