Improving Face Recognition from Videos with Preprocessed Representative Faces

In this work, a face recognition system working on video records is constructed The aim of this study is to analyze; how -instead of using all video frames and building a complex model-using only a subset of informative frames (named representative frames), automatically selected and preprocessed by a system, affects success rate and processing time. The basic idea in selecting the representative frames is that; faces that are captured from the front contain sufficient information for recognition. So a simple algorithm -that uses facial features (eyes and mouth) and positions of these features on the face- can be developed to extract these frames from the videos, Even the number of these frames is relatively small, handling these frames in a proper way and preprocessing them with some simple image processing techniques effect the recognition rate positively. After representative frames are extracted and preprocessed dimensional analyses are applied and extracted data is transformed onto a new space. Finally, transformed data is used to build and use a trainer, by which the system is trained with videos consisting of only one person per each, however having no restriction in pose, angle and rotation. Then different videos with same situations are presented to the trained classifier for recognition.