We propose an appearance based model for face recognition in news videos using an enormously large databank of still images. This is a step towards building an elaborate face-query system using multimodal audio-visual data. We use the fact that faces of the same person appear similar than of different people. We preprocess the videos, apply feature extraction, feature matching and a unique parallel line matching algorithm to develop a simple yet a powerful face recognition system. We tested our approach on real world data and the results show good performance both for high resolution still images and low resolution news videos without involving any training or tasks like face rectification, warping etc. It can be incorporated as part of a larger multimodal news video analysis system with problems of time alignment between text and faces. Our results show that this simple approach also works well where video modality is the only source of information.