In surveillance applications, a video can be an advantageous alternative to traditional still image face recognition. In order to extract discriminative features from a video, frame based processing is preferred; however, not all frames are suitable for face recognition. While some frames are distorted due to noise, blur, and occlusion, others might be affected by the presence of covariates such as pose, illumination, and expression. Frames affected by imaging artifacts such as noise and blur may not contain reliable facial information and may affect the recognition performance. In an ideal scenario, such frames should not be considered for feature extraction and matching. Furthermore, video contains a large amount of frames and adjacent frames contain largely redundant information and processing all the frames from a video increases the computational complexity. Instead of utilizing all the frames from a video, frame selection can be performed to determine a subset of frames which is best suited for face recognition. Several frame selection algorithms have been proposed in the literature to address these concerns. In this chapter, we discuss the role and importance of frame selection in video face recognition, provide an overview of existing techniques, present an entropy based frame selection algorithm with the results and analysis on Point-and-Shoot-Challenge which is a recent benchmark database, and also propose a new paradigm for frame selection algorithms as a path forward. © Springer International Publishing Switzerland 2016.