Using a Markov Network to Recognize People in Consumer Images

Abstract

Markov networks are an effective tool for the difficult but important problem of recognizing people in consumer image collections. Given a small set of labeled faces, we seek to recognize the other faces in an image collection. The constraints of the problem are exploited when forming the Markov network edge potentials. Inference is also used to suggest faces for the user to label, minimizing the work on the part of the user. In one test set containing 4 individuals, an 86% recognition rate is achieved with only 3 labeled examples.