Abstract
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 23rd International Conference on Pattern Recognition (ICPR) |
| Editors | Eduardo Bayro-Corrochano |
| Place of Publication | USA |
| Publisher | IEEE, Institute of Electrical and Electronics Engineers |
| Pages | 3026-3030 |
| ISBN (Print) | 9781509048465 |
| Publication status | Published - 2016 |
| Event | 2016 23rd International Conference on Pattern Recognition (ICPR) - Cancun, Mexico Duration: 4 Dec 2016 → 8 Dec 2016 |
Conference
| Conference | 2016 23rd International Conference on Pattern Recognition (ICPR) |
|---|---|
| Country | Mexico |
| City | Cancun |
| Period | 4/12/16 → 8/12/16 |
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Audio-Visual Biometric Recognition Via Joint Sparse Representations. / Primorac, Rudi; Togneri, Roberto; Bennamoun, Mohammed; Sohel, Ferdous.
Proceedings of the 23rd International Conference on Pattern Recognition (ICPR). ed. / Eduardo Bayro-Corrochano. USA : IEEE, Institute of Electrical and Electronics Engineers, 2016. p. 3026-3030.Research output: Chapter in Book/Conference paper › Conference paper
TY - GEN
T1 - Audio-Visual Biometric Recognition Via Joint Sparse Representations
AU - Primorac, Rudi
AU - Togneri, Roberto
AU - Bennamoun, Mohammed
AU - Sohel, Ferdous
PY - 2016
Y1 - 2016
N2 - In this paper we present a novel audio-visual (AV) person identification system based on joint sparse representation. Video features used were vectorized raw pixel values, while ivectors were used as the audio features. Classification is performed by solving the joint sparsity optimization problem, and fusion is carried out by using the quality (confidence) assigned to each matcher. Our experimental results on the challenging MOBIO database using 100 subjects show that the system based on joint sparse representation outperforms the system based on separate sparse representations for each modality. Furthermore, we show that our newly introduced quality measure improves the system’s performance, when compared to conventionally used quality measures for sparse representation - based systems.
AB - In this paper we present a novel audio-visual (AV) person identification system based on joint sparse representation. Video features used were vectorized raw pixel values, while ivectors were used as the audio features. Classification is performed by solving the joint sparsity optimization problem, and fusion is carried out by using the quality (confidence) assigned to each matcher. Our experimental results on the challenging MOBIO database using 100 subjects show that the system based on joint sparse representation outperforms the system based on separate sparse representations for each modality. Furthermore, we show that our newly introduced quality measure improves the system’s performance, when compared to conventionally used quality measures for sparse representation - based systems.
M3 - Conference paper
SN - 9781509048465
SP - 3026
EP - 3030
BT - Proceedings of the 23rd International Conference on Pattern Recognition (ICPR)
A2 - Bayro-Corrochano, Eduardo
PB - IEEE, Institute of Electrical and Electronics Engineers
CY - USA
ER -