Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/107230
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Electrical and Electronic Engineering | - |
dc.creator | Li, H | en_US |
dc.creator | Lam, KM | en_US |
dc.creator | Chiu, MY | en_US |
dc.creator | Wu, K | en_US |
dc.creator | Lei, Z | en_US |
dc.date.accessioned | 2024-06-13T01:04:45Z | - |
dc.date.available | 2024-06-13T01:04:45Z | - |
dc.identifier.isbn | 978-1-5090-6067-2 (Electronic) | en_US |
dc.identifier.isbn | 978-1-5090-6066-5 (USB) | en_US |
dc.identifier.isbn | 978-1-5090-6068-9 (Print on Demand(PoD)) | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/107230 | - |
dc.description | 2017 IEEE International Conference on Multimedia and Expo (ICME), 10-14 July 2017, Hong Kong, China | en_US |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers | en_US |
dc.rights | ©2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | en_US |
dc.rights | The following publication H. Li, K. -M. Lam, M. -Y. Chiu, K. Wu and Z. Lei, "Efficient likelihood Bayesian constrained local model," 2017 IEEE International Conference on Multimedia and Expo (ICME), Hong Kong, China, 2017, pp. 763-768 is available at https://doi.org/10.1109/ICME.2017.8019518. | en_US |
dc.subject | Bayesian constrained local models | en_US |
dc.subject | Face alignment | en_US |
dc.subject | Point distribution model | en_US |
dc.subject | Random forest | en_US |
dc.title | Efficient likelihood Bayesian constrained local model | en_US |
dc.type | Conference Paper | en_US |
dc.identifier.spage | 763 | en_US |
dc.identifier.epage | 768 | en_US |
dc.identifier.doi | 10.1109/ICME.2017.8019518 | en_US |
dcterms.abstract | The constrained local model (CLM) proposes a paradigm that the locations of a set of local landmark detectors are constrained to lie in a subspace, spanned by a shape point distribution model (PDM). Fitting the model to an object involves two steps. A response map, which represents the likelihood of locations for a landmark, is first computed for each landmark using local-texture detectors. Then, an optimal PDM is determined by jointly maximizing all the response maps simultaneously, with a global-shape constraint. This global optimization can be considered a Bayesian inference problem, where the posterior distribution of the shape parameters, as well as the pose parameters, can be inferred using maximum a posteriori (MAP). In this paper, based on the CLM model, we present a novel CLM variant, which employs random-forest regressors to estimate the location of each landmark, as a likelihood term, efficiently. This novel CLM framework is called efficient likelihood Bayesian constrained local model (elBCLM). Furthermore, in each stage of the regressors, the PDM local non-rigid parameters, i.e. the shape parameters, of the previous stage can work as shape clues for training the regressors for the current stage. To further improve the efficiency, we also propose a feature-switching scheme used in the cascaded framework. Experimental results on benchmark datasets show our approach achieves about 3 to 5 times speed-up, when compared with the existing CLM models, and improves by around 10% on fitting accuracy, when compared with the other regression-based models. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | In Proceedings of 2017 IEEE International Conference on Multimedia and Expo (ICME), 10-14 July 2017, Hong Kong, China, p. 763-768 | en_US |
dcterms.issued | 2017 | - |
dc.identifier.scopus | 2-s2.0-85030241123 | - |
dc.relation.conference | IEEE International Conference on Multimedia and Expo [ICME] | - |
dc.description.validate | 202404 bckw | - |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | EIE-0669 | - |
dc.description.pubStatus | Published | en_US |
dc.identifier.OPUS | 9604726 | - |
dc.description.oaCategory | Green (AAM) | en_US |
Appears in Collections: | Conference Paper |
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File | Description | Size | Format | |
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Lam_Efficient_Likelihood_Bayesian.pdf | Pre-Published version | 1.89 MB | Adobe PDF | View/Open |
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