Name
Abstract | ||
|---|---|---|
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. |
Year | Venue | Keywords |
|---|---|---|
2017 | 2017 24TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) | face discovery, face clustering, deep matching, bag-of-tracklets, egocentric photo-streams |
DocType | Volume | ISSN |
Conference | abs/1703.01790 | 1522-4880 |
Citations | PageRank | References |
3 | 0.42 | 4 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Maedeh Aghaei | 1 | 35 | 5.38 |
| Mariella Dimiccoli | 2 | 89 | 18.29 |
| P. Radeva | 3 | 115 | 13.89 |