This paper presents a number of new views and techniques claimed to be very important for the problem of face recognition in video (FRiV). First, a clear differentiation is made between photographic facial data and video-acquired facial data as being two different modalities: one providing hard biometrics, the other providing softer biometrics. Second, faces which have the resolution of at least 12 pixels between the eyes are shown to be recognizable by computers just as they are by humans. As a way to deal with low resolution and quality of each individual video frame, the paper offers to use the neuro-associative principle employed by human brain, according to which both memorization and recognition of data are done based on a flow of frames rather than on one frame: synaptic plasticity provides a way to memorize from a sequence, while the collective decision making over time is very suitable for recognition of a sequence. As a benchmark for FRiV approaches, the paper introduces the IIT-NRC video-based database of faces which consists of pairs of low-resolution video clips of unconstrained facial motions. The recognition rate of over 95%, which we achieve on this database, as well as the results obtained on real-time annotation of people on TV allow us to believe that the proposed framework brings us closer to the ultimate benchmark for the FRiV approaches, which is “if you are able to recognize a person, so should the computer”.