  Given an unconstrained stream of images captured by a wearable photo-camera
(2fpm), we propose an unsupervised bottom-up approach for automatic clustering
appearing faces into the individual identities present in these data. The
problem is challenging since images are acquired under real world conditions;
hence the visible appearance of the people in the images undergoes intensive
variations. Our proposed pipeline consists of first arranging the photo-stream
into events, later, localizing the appearance of multiple people in them, and
finally, grouping various appearances of the same person across different
events. Experimental results performed on a dataset acquired by wearing a
photo-camera during one month, demonstrate the effectiveness of the proposed
approach for the considered purpose.
