Abstract
| Original language | Undefined |
|---|---|
| Title of host publication | Proceedings of the IEEE International Conference on Image Processing, ICIP 2013 |
| Place of Publication | USA |
| Publisher | IEEE Computer Society |
| Pages | WA.L5.5 |
| Number of pages | 5 |
| ISBN (Print) | not assigned |
| Publication status | Published - Sep 2013 |
Publication series
| Name | |
|---|---|
| Publisher | IEEE Computer Society |
Keywords
- EWI-24551
- HMI-HF: Human Factors
- FDDB database
- IR-95219
- AFLW database
- Multi-View Face Detection
- METIS-310008
- Cascade Deformable Models
Cite this
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Empirical Analysis of Cascade Deformable Models for Multi-View Face Detection. / Orozco, J.; Martinez, B.; Pantic, Maja.
Proceedings of the IEEE International Conference on Image Processing, ICIP 2013. USA : IEEE Computer Society, 2013. p. WA.L5.5.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review
TY - GEN
T1 - Empirical Analysis of Cascade Deformable Models for Multi-View Face Detection
AU - Orozco, J.
AU - Martinez, B.
AU - Pantic, Maja
PY - 2013/9
Y1 - 2013/9
N2 - In this paper, we present a face detector based on Cascade Deformable Part Models (CDPM) [1]. Our model is learnt from partially labelled images using Latent Support Vector Machines (LSVM). Recently Zhu et al. [2] proposed a Tree StructureModel for multi-view face detection trained with facial landmark labels, which resulted on a complex and suboptimal system for face detection. Instead, we adopt CDPMs enhanced with a data-mining procedure to enrich models during the LSVM training. Furthermore, a post-optimization procedure is derived to improve the performance of the CDPMs. Experimental results show that the proposed model can deal with highly expressive and partially occluded faces while outperforming the state-of-the-art face detectors by a large margin on challenging benchmarks such as the FDDB [3] and the AFLW [4] databases.
AB - In this paper, we present a face detector based on Cascade Deformable Part Models (CDPM) [1]. Our model is learnt from partially labelled images using Latent Support Vector Machines (LSVM). Recently Zhu et al. [2] proposed a Tree StructureModel for multi-view face detection trained with facial landmark labels, which resulted on a complex and suboptimal system for face detection. Instead, we adopt CDPMs enhanced with a data-mining procedure to enrich models during the LSVM training. Furthermore, a post-optimization procedure is derived to improve the performance of the CDPMs. Experimental results show that the proposed model can deal with highly expressive and partially occluded faces while outperforming the state-of-the-art face detectors by a large margin on challenging benchmarks such as the FDDB [3] and the AFLW [4] databases.
KW - EWI-24551
KW - HMI-HF: Human Factors
KW - FDDB database
KW - IR-95219
KW - AFLW database
KW - Multi-View Face Detection
KW - METIS-310008
KW - Cascade Deformable Models
M3 - Conference contribution
SN - not assigned
SP - WA.L5.5
BT - Proceedings of the IEEE International Conference on Image Processing, ICIP 2013
PB - IEEE Computer Society
CY - USA
ER -