Automatic Facial Expression Recognition Using Facial Animation Parameters and Multistream HMMs

Aleksic, Petar S.; Katsaggelos, Aggelos K.

doi:10.1109/tifs.2005.863510

Cited by 197 publications

(91 citation statements)

References 24 publications

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“…Some papers attempt to recognize a small set of prototypical emotional expressions, i.e. joy, surprise, anger, sadness, fear, and disgust, for example [14], [15], [16]. Yeasin et al [14] used the horizontal and vertical components of the flow as features.…”

Section: Local Descriptors For Facial Image Analysismentioning

confidence: 99%

“…This approach cannot deal with illumination variations, however. Aleksic and Katsaggelos [15] proposed facial animation parameters as features describing facial expressions, and utilized multi-stream HMMs for recognition. The system is complex, making it difficult to perform in real-time.…”

Section: Local Descriptors For Facial Image Analysismentioning

confidence: 99%

“…15) and out-of-plane rotation (subjects S045 and S051 in the database, permission not granted to be shown in publications) of the head -no further alignment of facial features, such as alignment of the mouth, was performed in our algorithm. We do not need to 1) track faces or eyes in the following frames as in [38], 2) segment eyes and outer-lips [15], 3) select constant illumination [15] or perform illumination correction [14], 4) align facial features with respect to the canonical template [14] or normalize the faces to a fixed size as done by most of the papers [11], [12], [13], [38]. In this way our experimental setup is more realistic.…”

Section: B Facial Expression Recognition Experiments 1) Datasetmentioning

confidence: 99%

“…Most of the research on facial expression recognition has been based on static images [7], [8], [9], [10], [11], [12], [13]. Some research on using facial dynamics has also been carried out [14], [15], [16]; however, reliable segmentation of lips and other moving facial parts in natural environments has proved to be a major problem. Our approach is completely different, avoiding error prone segmentation.…”

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confidence: 99%

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Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions

Zhao

Pietikäinen

2007

IEEE Trans. Pattern Anal. Mach. Intell.

2,461

1,625

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Abstract-Dynamic texture is an extension of texture to the temporal domain. Description and recognition of dynamic textures have attracted growing attention. In this paper, a novel approach for recognizing dynamic textures is proposed and its simplifications and extensions to facial image analysis are also considered. First, the textures are modeled with volume local binary patterns (VLBP), which are an extension of the LBP operator widely used in ordinary texture analysis, combining motion and appearance. To make the approach computationally simple and easy to extend, only the co-occurrences on three orthogonal planes (LBP-TOP) are then considered. A block-based method is also proposed to deal with specific dynamic events, such as facial expressions, in which local information and its spatial locations should also be taken into account. In experiments with two dynamic texture databases, DynTex and MIT, both the VLBP and LBP-TOP clearly outperformed the earlier approaches. The proposed block-based method was evaluated with the CohnKanade facial expression database with excellent results. Advantages of our approach include local processing, robustness to monotonic gray-scale changes and simple computation.

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Section: Local Descriptors For Facial Image Analysismentioning

confidence: 99%

Section: Local Descriptors For Facial Image Analysismentioning

confidence: 99%

Section: B Facial Expression Recognition Experiments 1) Datasetmentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions

Zhao

Pietikäinen

2007

IEEE Trans. Pattern Anal. Mach. Intell.

2,461

1,625

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show abstract

“…There are 2 -4 images for every face expression, and images are all 256 × 256 grayscale images. Our self-built database composes of seven individuals, each person also shows seven basic facial expressions, and pictures are also 128 × 128 grayscale images [3,8]. As images obtain from two different databases, there are very huge difference.…”

Section: Facial Expression Image Databasementioning

confidence: 99%

The Study of Multi-Expression Classification Algorithm Based on Adaboost and Mutual Independent Feature

Lang¹,

Hu²

2011

JSIP

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In the paper conventional Adaboost algorithm is improved and local features of face such as eyes and mouth are separated as mutual independent elements for facial feature extraction and classification. The multi-expression classification algorithm which is based on Adaboost and mutual independent feature is proposed. In order to effectively and quickly train threshold values of weak classifiers of features, Sample of training is carried out simple improvement. We obtain a good classification results through experiments.

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Face Image Processing and Analysis

Shih

2010

Image Processing and Pattern Recognition

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Automatic Facial Expression Recognition Using Facial Animation Parameters and Multistream HMMs

Cited by 197 publications

References 24 publications

Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions

Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions

The Study of Multi-Expression Classification Algorithm Based on Adaboost and Mutual Independent Feature

Face Image Processing and Analysis

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