Significant progress has been made in addressing face recognition channel, sensor, and session effects in both still images and video. These effects include the classic PIE (pose, illumination, expression) variation, as well as variations in other characteristics such as age and facial hair. While much progress has been made, there has been little formal work in characterizing and compensating for the intrinsic differences between faces in still images and video frames. These differences include that faces in still images tend to have neutral expressions and frontal poses, while faces in videos tend to have more natural expressions and poses. Typically faces in videos are also blurrier, have lower resolution, and are framed differently than faces in still images. Addressing these issues is important when comparing face images between still images and video frames. Also, face recognition systems for video applications often rely on legacy face corpora of still images and associated meta data (e.g. identifying information, landmarks) for development, which are not formally compensated for when applied to the video domain. In this paper we will evaluate the impact of channel effects on face recognition across still images and video frames for the search and retrieval task. We will also introduce a novel face recognition approach for addressing the performance gap across these two respective channels. The datasets and evaluation protocols from the Labeled Faces in the Wild (LFW) still image and YouTube Faces (YTF) video corpora will be used for the comparative characterization and evaluation. Since the identities of subjects in the YTF corpora are a subset of those in the LFW corpora, this enables an apples-to-apples comparison of in-corpus and cross-corpora face comparisons.