The looking back screens

Uyl, Marten den; Kuilenburg, Hans van; Ivan, Pirner; Israel, Maya

doi:10.1109/afgr.2008.4813426

Cited by 3 publications

(4 citation statements)

References 3 publications

(2 reference statements)

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“…Our research has highlighted the importance of incorporating naturalistic observations in training AFC models, addressing limitations associated with relying solely on standardised and controlled environments. Whilst FaceReader, a widely used AFC software ( Den Uyl and Van Kuilenburg, 2005 ; Noldus, 2014 ), has shown efficacy in specific conditions ( Höfling et al, 2021 ), challenges persist in accurately distinguishing between neutral and unpleasant stimuli and effectively handling non-standardised expressions ( Höfling et al, 2020 ; Küntzler et al, 2021 ). Nevertheless, AFC has demonstrated promise beyond academic research, particularly in predicting self-reported emotions and providing valuable insights in marketing research, where it captures non-verbal aspects ( Höfling and Alpers, 2023 ).…”

Section: Discussionmentioning

confidence: 99%

“…The software has been validated and found to outperform human coders, correctly identifying 88% of expressions compared to 85% by humans ( Lewinski et al, 2014 ). FaceReader demonstrates high accuracy in classifying various expressions, with rates reported at 94% for Neutral, 82% for Scared, and other studies reporting performance rates ranging from 80 to 89% ( Den Uyl and Van Kuilenburg, 2005 ; Terzis et al, 2013 ; Skiendziel et al, 2019 ).…”

Section: Introductionmentioning

confidence: 86%

“…FaceReader is a general-purpose automated facial coding (AFC) software developed by Noldus, which employs a three-step process—face finding, modelling, and classifying—to accurately determine facial expressions ( Den Uyl and Van Kuilenburg, 2005 ; Noldus, 2014 ). It offers classification into eight distinct expressions, including Happy, Sad, Angry, Scared, Surprised, Disgusted, Neutral, and Contempt.…”

Section: Introductionmentioning

confidence: 99%

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Quantifying the efficacy of an automated facial coding software using videos of parents

Burgess,

Culpin,

Costantini

et al. 2023

Front. Psychol.

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IntroductionThis work explores the use of an automated facial coding software - FaceReader - as an alternative and/or complementary method to manual coding.MethodsWe used videos of parents (fathers, n = 36; mothers, n = 29) taken from the Avon Longitudinal Study of Parents and Children. The videos—obtained during real-life parent-infant interactions in the home—were coded both manually (using an existing coding scheme) and by FaceReader. We established a correspondence between the manual and automated coding categories - namely Positive, Neutral, Negative, and Surprise - before contingency tables were employed to examine the software’s detection rate and quantify the agreement between manual and automated coding. By employing binary logistic regression, we examined the predictive potential of FaceReader outputs in determining manually classified facial expressions. An interaction term was used to investigate the impact of gender on our models, seeking to estimate its influence on the predictive accuracy.ResultsWe found that the automated facial detection rate was low (25.2% for fathers, 24.6% for mothers) compared to manual coding, and discuss some potential explanations for this (e.g., poor lighting and facial occlusion). Our logistic regression analyses found that Surprise and Positive expressions had strong predictive capabilities, whilst Negative expressions performed poorly. Mothers’ faces were more important for predicting Positive and Neutral expressions, whilst fathers’ faces were more important in predicting Negative and Surprise expressions.DiscussionWe discuss the implications of our findings in the context of future automated facial coding studies, and we emphasise the need to consider gender-specific influences in automated facial coding research.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 86%

Section: Introductionmentioning

confidence: 99%

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Quantifying the efficacy of an automated facial coding software using videos of parents

Burgess,

Culpin,

Costantini

et al. 2023

Front. Psychol.

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“…This emotional expression is uniquely and reliably recognized in healthy controls with high specificity and sensitivity. Furthermore, given that the video analysis software recognizes happiness similarly to objective human observers with accuracy greater than 90%, its ability to detect happiness after face transplant at subclinical levels not perceptible to human observers highlights the potential of video analysis software as a rehabilitative tool.…”

Section: Discussionmentioning

confidence: 99%

Recognizing Emotional Expression as an Outcome Measure After Face Transplant

et al. 2020

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IMPORTANCE Limited quantitative data exist on the restoration of nonverbal communication via facial emotional expression after face transplant. Objective and noninvasive methods for measuring outcomes and tracking rehabilitation after face transplant are lacking. OBJECTIVE To measure emotional expression as an indicator of functional outcomes and rehabilitation after face transplant via objective, noninvasive, and nonobtrusive software-based video analysis. DESIGN, SETTING, AND PARTICIPANTS This single-center case-control study analyzed videos with commercially available video analysis software capable of detecting emotional expression. The study participants were 6 patients who underwent face transplant at Brigham and Women's Hospital between April 2009 and March 2014. They were matched by age, race/ethnicity, culture, and sex to 6 healthy controls with no prior facial surgical procedures. Participants were asked to perform either emotional expressions (direct evaluation) or standardized facial movements (indirect evaluation). Videos were obtained in a clinical setting, except for direct evaluation videos of 3 patients that were recorded at the patients' residences. Data analysis was performed from June 2018 to November 2018. MAIN OUTCOMES AND MEASURES The possibility of detecting the emotional expressions of happiness, sadness, anger, fear, surprise, and disgust was evaluated using intensity score values between 0 and 1, representing expressions that are absent or fully present, respectively. RESULTS Six patients underwent face transplant (4 men; mean [SD] age, 42 [14] years). Four underwent full face transplants, and 2 underwent partial face transplants of the middle and lower two-thirds of the face. In healthy controls, happiness was the only emotion reliably recognized in both indirect (mean [SD] intensity score, 0.92 [0.05]) and direct (mean [SD] intensity score, 0.91 [0.04]) evaluation. Indirect evaluation showed that expression of happiness significantly improved 1 year after transplant (0.04 point per year; 95% CI, 0.02 to 0.06 point per year; P = .002). Expression of happiness was restored to a mean of 43% (range, 14% to 75%) of that of healthy controls after face transplant. The expression of sadness showed a significant change only during the first year after transplant (−0.53 point per year; 95% CI, −0.82 to −0.24 point per year; P = .005). All other emotions were detectable with no significant change after transplant. Nearly all emotions were detectable in long-term direct evaluation of 3 patients, with expression of happiness restored to a mean of 26% (range, 5% to 59%) of that of healthy controls. CONCLUSIONS AND RELEVANCE Partial restoration of facial emotional expression is possible after face transplant. Video analysis software may provide useful clinical information and aid rehabilitation after face transplant.

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Toward Emotion Recognition Embodied in Social Robots: Implementation of Laban Movement Analysis into NAO Robot

Arent¹,

Gakis²,

Sobecki³

et al. 2018

JAMRIS

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The looking back screens

Abstract: The Looking Back Screens demonstrator presents a high end 'multi-level' face analysis system that observes facial characteristics, expressions, gaze and (facial) gestures of viewers and integrates these viewer observations in various interactive 'games' with an infocus viewer .

Cited by 3 publications

References 3 publications

Quantifying the efficacy of an automated facial coding software using videos of parents

Quantifying the efficacy of an automated facial coding software using videos of parents

Recognizing Emotional Expression as an Outcome Measure After Face Transplant

Toward Emotion Recognition Embodied in Social Robots: Implementation of Laban Movement Analysis into NAO Robot

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