Quantification of facial expressions using high-dimensional shape transformations

Verma, Ragini; Davatzikos, Christos; Loughead, James; Indersmitten, Tim; Hu, Ranliang; Kohler, Christian G.; Gur, Raquel E.; Gur, Ruben C.

doi:10.1016/j.jneumeth.2004.05.016

Cited by 33 publications

(36 citation statements)

References 35 publications

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“…A wide range of deformable models, such as muscle-based models (Ohta et al, 1998), a 3D wireframe model (Cohen et al, 2003b), a facial mesh model Pentland, 1995, 1997), a potential net model (Kimura and Yachida, 1997), ASM (Lanitis et al, 1997), and a geometry-based shape model (Verma et al, 2005;Davatzikos, 2001), are used to track facial features in video. Although it has been demonstrated that a sophisticated deformable model can improve facial tracking accuracy, thereby improving facial expression analysis accuracy (Wen and Huang, 2003), there are no comprehensive experiments showing which deformable model is superior to the others.…”

Section: Video-based Methodsmentioning

confidence: 99%

“…9. There are six grayscale stereo camera, one color camera and a video camera (Verma et al, 2005). The color camera captures the 2D images, work on which has been described in Alvino et al (2007).…”

Section: Data Collectionmentioning

confidence: 99%

“…There are already many AFEA methods being presented in both clinical and computer vision communities (Gaebel and Wölwer, 1992;Hellewell et al, 1994;Schneider et al, 1990;Pantic and Rothkrantz, 2000;Fasel and Luettin, 2003;Tian et al, 2005). Most of the current AFEA methods focus on the recognition of posed facial expressions with application to human computer interaction tasks, and only a few of them have been applied to clinical studies (Verma et al, 2005;Alvino et al, 2007). In previous work on expression quantification (Verma et al, 2005;Alvino et al, 2007), the expression changes were modeled using elastic shape transformations between the face of a neutral template and the corresponding emotionally expressive face.…”

Section: Introductionmentioning

confidence: 99%

“…Most of the current AFEA methods focus on the recognition of posed facial expressions with application to human computer interaction tasks, and only a few of them have been applied to clinical studies (Verma et al, 2005;Alvino et al, 2007). In previous work on expression quantification (Verma et al, 2005;Alvino et al, 2007), the expression changes were modeled using elastic shape transformations between the face of a neutral template and the corresponding emotionally expressive face. Again, as most of the current AFEA methods, this approach is based on static 2D images without any temporal component.…”

Section: Introductionmentioning

confidence: 99%

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Automated video-based facial expression analysis of neuropsychiatric disorders

Wang

Barrett

Martin

et al. 2008

Journal of Neuroscience Methods

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Deficits in emotional expression are prominent in several neuropsychiatric disorders, including schizophrenia. Available clinical facial expression evaluations provide subjective and qualitative measurements, which are based on static 2D images that do not capture the temporal dynamics and subtleties of expression changes. Therefore, there is a need for automated, objective and quantitative measurements of facial expressions captured using videos. This paper presents a computational framework that creates probabilistic expression profiles for video data and can potentially help to automatically quantify emotional expression differences between patients with neuropsychiatric disorders and healthy controls. Our method automatically detects and tracks facial landmarks in videos, and then extracts geometric features to characterize facial expression changes. To analyze temporal facial expression changes, we employ probabilistic classifiers that analyze facial expressions in individual frames, and then propagate the probabilities throughout the video to capture the temporal characteristics of facial expressions. The applications of our method to healthy controls and case studies of patients with schizophrenia and Asperger's syndrome demonstrate the capability of the video-based expression analysis method in capturing subtleties of facial expression. Such results can pave the way for a video-based method for quantitative analysis of facial expressions in clinical research of disorders that cause affective deficits.

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Section: Video-based Methodsmentioning

confidence: 99%

Section: Data Collectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Automated video-based facial expression analysis of neuropsychiatric disorders

Wang

Barrett

Martin

et al. 2008

Journal of Neuroscience Methods

Self Cite

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show abstract

“…For patients with trouble forming facial expressions due to facial paralysis, objective quantitative measures of facial expression have been used to evaluate medical and surgical treatments (Hontanilla & Aubá, 2008;Johnson, Bajaj-Luthra, Llull, & Johnson, 1997;Sawyer, See, & Nduka, 2010). Other work has quantified expression differences between patients with neuropsychiatric disorders such as schizophrenia and healthy controls (Alvino et al, 2007;Verma et al, 2005;Wang et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

Associations between psychosocial functioning and smiling intensity in patients with head and neck cancer

Lee

Teo

Guindani

et al. 2014

Psychology, Health & Medicine

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Increasing attention is being given to developing quantitative measures of facial expression. This study used quantitative facial expression analysis to examine associations between smiling intensity and psychosocial functioning in patients with head and neck cancer (HNC). Smiling intensity of 95 HNC patients was measured using 48 quantitative measures calculated from facial photographs with and without a smile. We computed a composite smiling intensity score for each patient representing the degree of similarity to healthy controls. A lower composite score indicates that the person is less expressive, on average, than healthy controls. Patients also completed self-report measures assessing domains of body image and quality of life (QOL). Spearman rank correlations were computed to examine relationships between composite scores and psychosocial functioning. Composite scores were significantly correlated with multiple measures of body image and QOL. Specifically, decreased smiling intensity was associated with feelings of dissatisfaction with one's body, perceived negative social impact of body image, increased use of avoidance as a body image-coping strategy, reduced functional well-being, and greater head and neck cancer-specific issues. To the best of our knowledge, this is the first study to demonstrate associations between an objectively quantified facial expression (i.e. smiling) and psychosocial functioning. Most previous studies have measured facial expression qualitatively. These findings indicate that smiling intensity may serve as an important clinical indicator of psychosocial well-being and warrants further clinical investigation.

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A Multilevel Fusion Approach for Audiovisual Emotion Recognition

Chetty¹,

Wagner²

2015

Emotion Recognition

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Quantification of facial expressions using high-dimensional shape transformations

Cited by 33 publications

References 35 publications

Automated video-based facial expression analysis of neuropsychiatric disorders

Automated video-based facial expression analysis of neuropsychiatric disorders

Associations between psychosocial functioning and smiling intensity in patients with head and neck cancer

A Multilevel Fusion Approach for Audiovisual Emotion Recognition

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