Person Identification Using Text and Image Data

This paper presents a bimodal identification system using text based term vectors and EBGM face recognition. Identification was tested on a database of 118 celebrities downloaded from the internet. The dataset contained multiple images and two biographies for each person. Text based identification had a 100% identification rate for the full biographies. When the text data was artificially restricted to six sentences per subject, rank one identification rates were similar to face recognition (approx. 22%). In this restricted case, combining text identification and face identification showed a significant improvement in the identification rate over either method alone.