The DOI matches its Crossref record; 32 of the 34 references it cites were found in a registry or an index.
Automatic Facial Expression Recognition Using Facial Animation Parameters and Multistream HMMs
Abstract
The performance of an automatic facial expression recognition system can be significantly improved by modeling the reliability of different streams of facial expression information utilizing multistream hidden Markov models (HMMs). In this paper, we present an automatic multistream HMM facial expression recognition system and analyze its performance. The proposed system utilizes facial animation parameters (FAPs), supported by the MPEG-4 standard, as features for facial expression classification. Specifically, the FAPs describing the movement of the outer-lip contours and eyebrows are used as observations. Experiments are first performed employing single-stream HMMs under several different scenarios, utilizing outer-lip and eyebrow FAPs individually and jointly. A multistream HMM approach is proposed for introducing facial expression and FAP group dependent stream reliability weights. The stream weights are determined based on the facial expression recognition results obtained when FAP streams are utilized individually. The proposed multistream HMM facial expression system, which utilizes stream reliability weights, achieves relative reduction of the facial expression recognition error of 44% compared to the single-stream HMM system.
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“Is is rarely used to parametrize FEs for recognition purposes [136], [137].”
From Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-Related Applications (2016) · cites this paper as [137]
In the citing paper
“Most of these early works used geometric representations, like vectors for describing the motion of the face [134], active contours for describing the shape of the mouth and eyebrows [137], or deformable 2D mesh models [116].”
From Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-Related Applications (2016) · cites this paper as [137]
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Metadata
- Authors
- Aleksic, P. S., & Katsaggelos, A. K.
- Affiliations
- Northwestern University (US)
- ORCID
- Katsaggelos, A.K.
- Venue
- IEEE Transactions on Information Forensics and Security
- Published
- 2006-03
- Volume
- 1
- Issue
- 1
- Pages
- 3-11
- ISSN
- 1556-6013 (ISSN-L)
- OpenAlex
- W2098044144
- Cited by
- 235
- References
- 34
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 4 · 2023: 3 · 2022: 11 · 2021: 1 · 2020: 14 · 2019: 8 · 2018: 7 · 2017: 15 · 2016: 11 · 2015: 20 · 2014: 19 · 2013: 19 · 2012: 12
- FWCI
- 24.02
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Information Forensics and Security (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face and Expression Recognition
- Keywords
- facial expression recognition, facial animation parameters, eyebrow movements, MPEG-4, error reduction
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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