Name
Abstract | ||
|---|---|---|
This paper presents a low vision assistance system for individuals with blind spots in their visual field. The system identifies prominent faces in the field of view and redisplays them in regions that are visible to the user. As part of the system performance evaluation, we compare various algorithms for face detection and tracking on an Android smartphone, a netbook and a high-performance workstation representative of cloud computing. We examine processing time and energy consumption on all three platforms to determine the tradeoff between processing on a smartphone versus a cloud-desktop after compression and transmission. Our results demonstrate that Viola-Jones face detection along with Lucas-Kanade tracking achieve the best performance and efficiency. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/MWSCAS.2012.6292235 | Circuits and Systems |
Keywords | Field | DocType |
cloud computing,face recognition,handicapped aids,object detection,object tracking,smart phones,android smartphones,lucas-kanade tracking,viola-jones face detection,blind spots,cloud-desktop,energy consumption,high-performance workstation,low vision assistance system,netbook,processing time,visual field,face,face detection,visualization,support vector machines | Facial recognition system,Object detection,Computer vision,Android (operating system),Object-class detection,Computer science,Workstation,Video tracking,Artificial intelligence,Face detection,Cloud computing | Conference |
ISSN | ISBN | Citations |
1548-3746 E-ISBN : 978-1-4673-2525-7 | 978-1-4673-2525-7 | 1 |
PageRank | References | Authors |
0.35 | 4 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andreas Savakis | 1 | 377 | 41.10 |
| mark stump | 2 | 1 | 0.35 |
| Grigorios Tsagkatakis | 3 | 122 | 21.53 |
| roy w melton | 4 | 1 | 0.35 |
| gary behm | 5 | 1 | 0.35 |
| gwen sterns | 6 | 1 | 0.35 |