Discrimination Between Genuine Versus Fake Emotion Using Long-Short Term Memory with Parametric Bias and Facial Landmarks

Kim, Yong-Guk; Huynh, Xuan-Phung

doi:10.1109/iccvw.2017.362

Cited by 12 publications

(8 citation statements)

References 15 publications

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“…Using a hierarchical predictive processing framework, we demonstrated that predictive learning of facial features is sufficient for the self-organization of emotional categories in the higher level of network hierarchy without explicit emotional labels provided. Related to this finding, a previous study reported that self-organized higherlevel neural representation can be used to discriminate genuine and fake emotions from facial movies using a hierarchical RNN with PB (35). Combined with our findings, this suggests that the extraction of abstract information from dynamic facial expressions can be understood using the predictive processing framework.…”

Section: Discussionsupporting

confidence: 86%

Neural network modeling of altered facial expression recognition in autism spectrum disorders based on predictive processing framework

Takahashi¹,

Murata²,

Idei³

et al. 2021

Preprint

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BackgroundThe mechanism underlying the emergence of emotional categories from visual facial expression information during the developmental process is largely unknown. Therefore, this study proposes a system-level explanation for understanding the facial emotion recognition process and its alteration in autism spectrum disorder (ASD) from the perspective of predictive processing theory.MethodsPredictive processing for facial emotion recognition was implemented as a hierarchical recurrent neural network (RNN). The RNNs were trained to predict the dynamic changes of facial expression movies for six basic emotions without explicit emotion labels as a developmental learning process, and were evaluated by the performance of recognizing unseen facial expressions for the test phase. In addition, the causal relationship between the network characteristics assumed in ASD and ASD-like cognition was investigated.ResultsAfter the developmental learning process, emotional categories emerged in the natural course of self-organization in higher-level neurons, even though emotional labels were not explicitly instructed. In addition, the network successfully recognized unseen test facial sequences by adjusting higher-level activity through the process of minimizing precision-weighted prediction error. In contrast, the network simulating altered intrinsic neural excitability demonstrated reduced generalization capability and impaired emotional categorization in higher-level neurons. Consistent with previous findings from human behavioral studies, an excessive precision estimation of noisy details underlies this ASD-like cognition.ConclusionsThese results support the idea that impaired facial emotion recognition in ASD can be explained by altered predictive processing, and provide possible insight for investigating the neurophysiological basis of affective contact.

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Section: Discussionsupporting

confidence: 86%

Neural network modeling of altered facial expression recognition in autism spectrum disorders based on predictive processing framework

Takahashi¹,

Murata²,

Idei³

et al. 2021

Preprint

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“…It was thought that this result was due to the fact that posed expressions showed more prototypical facial features of the emotions (e.g., downturned mouth and furled brow for sadness) enabling both humans and AI to learn and recognize the posed emotions with higher accuracy. Spontaneous emotional expressions have subtle, but substantial differences compared to posed expressions of emotion, with changes in small muscles and less prototypical facial expressions (Kim and Huynh, 2017). Few studies have compared FER for posed and genuine FEs with mixed results.…”

Section: Posed Vs Genuine Facial Expressions Of Emotionmentioning

confidence: 99%

“…There are prototypical signs exhibited for some expressions of emotion, while genuine expressions of the same emotion are more complex and harder to interpret. For example, the expression of sadness when posed includes an out-turned lower lip, though spontaneous expressions of sadness are much more highly variable and often do not include this prototypical expression (Kim and Huynh, 2017). The class of smile expressions has received special attention regarding posed vs. genuine distinction (Blampied, 2008;Boraston et al, 2008).…”

Section: Posed Vs Genuine Facial Expressions Of Emotionmentioning

confidence: 99%

Review: Posed vs. Genuine Facial Emotion Recognition and Expression in Autism and Implications for Intervention

Webster

Wang

2021

Front. Psychol.

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Different styles of social interaction are one of the core characteristics of autism spectrum disorder (ASD). Social differences among individuals with ASD often include difficulty in discerning the emotions of neurotypical people based on their facial expressions. This review first covers the rich body of literature studying differences in facial emotion recognition (FER) in those with ASD, including behavioral studies and neurological findings. In particular, we highlight subtle emotion recognition and various factors related to inconsistent findings in behavioral studies of FER in ASD. Then, we discuss the dual problem of FER – namely facial emotion expression (FEE) or the production of facial expressions of emotion. Despite being less studied, social interaction involves both the ability to recognize emotions and to produce appropriate facial expressions. How others perceive facial expressions of emotion in those with ASD has remained an under-researched area. Finally, we propose a method for teaching FER [FER teaching hierarchy (FERTH)] based on recent research investigating FER in ASD, considering the use of posed vs. genuine emotions and static vs. dynamic stimuli. We also propose two possible teaching approaches: (1) a standard method of teaching progressively from simple drawings and cartoon characters to more complex audio-visual video clips of genuine human expressions of emotion with context clues or (2) teaching in a field of images that includes posed and genuine emotions to improve generalizability before progressing to more complex audio-visual stimuli. Lastly, we advocate for autism interventionists to use FER stimuli developed primarily for research purposes to facilitate the incorporation of well-controlled stimuli to teach FER and bridge the gap between intervention and research in this area.

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“…In [9], they collected a database by inducing emotional state on participants through videos exposed. In each video the participants started with a neutral emotion and then they expressed fake or genuine facial emotion.…”

Section: The Current Literature On Emotion Prediction From Face Imagesmentioning

confidence: 99%

“…In this context, there are many solutions on the literature for automatic identification of human emotions [2], [3], [4], [5], [6], [7]. On the other hand, there are situations that emotions can be necessarily not genuine, they can be acted, and have few classifiers to differentiate emotions from acted to genuine [8], [9], [10].…”

Section: Introductionmentioning

confidence: 99%

Using neural and distance-based machine learning techniques in order to identify genuine and acted emotions from facial expressions

Budke

Abreu

2021

11th International Conference of Pattern Recognition Systems (ICPRS 2021)

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Facial expressions are part of human non-verbal communication. Automatically discriminating between genuine and acted emotion can help psychologists, judges, human-machine interface, and so on. The problems for researchers starts when there are few real emotion facial datasets available, and thus, most of experimentation for evaluation is done by using fake emotions from actors. Thus, this paper explores the problem of classifying emotions from facial expressions as genuine or acted. We propose to extract facial features from images and to classify using k-Means, k-Nearest Neighbor and Neural Network. The best results obtained presented a promising 98.6% of precision for happiness emotion and 92% for sadness emotion.

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Discrimination Between Genuine Versus Fake Emotion Using Long-Short Term Memory with Parametric Bias and Facial Landmarks

Cited by 12 publications

References 15 publications

Neural network modeling of altered facial expression recognition in autism spectrum disorders based on predictive processing framework

Neural network modeling of altered facial expression recognition in autism spectrum disorders based on predictive processing framework

Review: Posed vs. Genuine Facial Emotion Recognition and Expression in Autism and Implications for Intervention

Using neural and distance-based machine learning techniques in order to identify genuine and acted emotions from facial expressions

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