Unsupervised face recognition by associative chaining

We propose a novel method for unsupervised face recognition from time-varying sequences of face images obtained in real-world environments. The method utilizes the higher level of sensory variation contained in the input image sequences to autonomously organize the data in an incrementally built graph structure, without relying on category-specific information provided in advance. This is achieved by “chaining” together similar views across the spatio-temporal representations of the face sequences in image space by two types of connecting edges depending on local measures of similarity. Experiments with real-world data gathered over a period of several months and including both frontal and side-view faces from 17 different subjects were used to test the method, achieving correct self-organization rate of 88.6%. The proposed method can be used in video surveillance systems or for content-based information retrieval.