All the people around me: face discovery in egocentric photo-streams
This addresses the challenge of face discovery in egocentric photo-streams under real-world conditions, which is incremental as it builds on existing methods for unsupervised clustering in dynamic environments.
The paper tackles the problem of automatically clustering faces into individual identities from an unconstrained stream of images captured by a wearable camera (2fpm), using an unsupervised bottom-up approach that arranges the stream into events, localizes people, and groups appearances across events, with experimental results on a one-month dataset demonstrating effectiveness.
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.