Cross-spectral image matching is a challenging research problem motivated by various applications, including surveillance, security, and identity management in general. An example of this problem includes cross-spectral matching of active infrared (IR) or thermal IR face images against a dataset of visible light images. A summary of recent developments in the field of cross-spectral face recognition by the authors is presented. In particular, it describes the original form and two variants of a local operator named composite multilobe descriptor (CMLD) for facial feature extraction with the purpose of cross-spectral matching of near-IR, short-wave IR, mid-wave IR, and long-wave IR to a gallery of visible light images. The experiments demonstrate that the variants of CMLD outperform the original CMLD and other recently developed composite operators used for comparison. In addition to different IR spectra, various standoff distances from close-up (1.5 m) to intermediate (50 m) and long (106 m) are also investigated. Performance of CMLD I to III is evaluated for each of the three cases of distances. The newly developed operators, CMLD I to III, are further utilized to conduct a study on cross-spectral partial face recognition where different facial regions are compared in terms of the amount of useful information they contain for the purpose of conducting cross-spectral face recognition. The experimental results show that among three facial regions considered in the experiments the eye region is the most informative for all IR spectra at all standoff distances.