Projects per year
Abstract
We present a dual-stream CNN that learns both appearance and facial features in tandem from still images and, after feature fusion, infers person identities. We then describe an alternative architecture of a single, lightweight ID-CondenseNet where a face detector-guided DC-GAN is used to generate distractor person images for enhanced training. For evaluation, we test both architectures on FLIMA, a new extension of an existing person re-identification dataset with added frame-by-frame annotations of face presence. Although the dual-stream CNN can outperform the CondenseNet approach on FLIMA, we show that the latter surpasses all state-of-the-art architectures in top-1 ranking performance when applied to the largest existing person re-identification dataset, MSMT17. We conclude that whilst re-identification performance is highly sensitive to the structure of datasets, distractor augmentation and network compression have a role to play for enhancing performance characteristics for larger scale applications.
Original language | English |
---|---|
Title of host publication | International Conference on Image Analysis and Processing |
Subtitle of host publication | Lecture Notes in Computer Science |
Publisher | Springer |
Pages | 488-498 |
Number of pages | 11 |
Volume | 11751 |
ISBN (Electronic) | 9783030306427 |
ISBN (Print) | 9783030306410 |
DOIs | |
Publication status | Published - 2 Sep 2019 |
Publication series
Name | Lecture Notes in Computer Science |
---|---|
Publisher | Springer Cham |
Volume | 11751 |
ISSN (Electronic) | 0302-9743 |
Structured keywords
- Digital Health
- SPHERE
Fingerprint
Dive into the research topics of 'Deep Compact Person Re-Identification with Distractor Synthesis via Guided DC-GANs'. Together they form a unique fingerprint.Projects
- 1 Active
-
SPHERE2
Craddock, I. J., Mirmehdi, M., Piechocki, R. J., Flach, P. A., Oikonomou, G., Burghardt, T., Damen, D., Santos-Rodriguez, R., O'Kane, A. A., McConville, R., Masullo, A. & Gooberman-Hill, R.
1/10/18 → 31/01/23
Project: Research, Parent