Using Temporal Features of Observers’ Physiological Measures to Distinguish Between Genuine and Fake Smiles

Hossain, Zakir; Gedeon, Tom; Sankaranarayana, Ramesh

doi:10.1109/taffc.2018.2878029

Cited by 16 publications

(12 citation statements)

References 47 publications

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“…Following previous research into physiological signal analysis [ 23 , 31 , 32 , 33 , 34 ], we extracted 29 features from each emotion sample, which include 15 time-domain features, 13 frequency-domain features and 1 nonlinear feature. Table 1 lists details of these extracted features.…”

Section: Methodsologymentioning

confidence: 99%

Emotion Recognition Based on Skin Potential Signals with a Portable Wireless Device

Chen

et al. 2021

Sensors

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Emotion recognition is of great importance for artificial intelligence, robots, and medicine etc. Although many techniques have been developed for emotion recognition, with certain successes, they rely heavily on complicated and expensive equipment. Skin potential (SP) has been recognized to be correlated with human emotions for a long time, but has been largely ignored due to the lack of systematic research. In this paper, we propose a single SP-signal-based method for emotion recognition. Firstly, we developed a portable wireless device to measure the SP signal between the middle finger and left wrist. Then, a video induction experiment was designed to stimulate four kinds of typical emotion (happiness, sadness, anger, fear) in 26 subjects. Based on the device and video induction, we obtained a dataset consisting of 397 emotion samples. We extracted 29 features from each of the emotion samples and used eight well-established algorithms to classify the four emotions based on these features. Experimental results show that the gradient-boosting decision tree (GBDT), logistic regression (LR) and random forest (RF) algorithms achieved the highest accuracy of 75%. The obtained accuracy is similar to, or even better than, that of other methods using multiple physiological signals. Our research demonstrates the feasibility of the SP signal’s integration into existing physiological signals for emotion recognition.

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Section: Methodsologymentioning

confidence: 99%

Emotion Recognition Based on Skin Potential Signals with a Portable Wireless Device

Chen

et al. 2021

Sensors

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“…People often fabricate smiles in everyday lives ( Ekman et al, 1988 ). Previous studies show that these can be distinguished from spontaneous smiles by differences in muscle patterns involved in the production of facial expressions ( Ekman and Friesen, 1982 ; Ekman et al, 1988 ; Ekman, 1993 ; Kanade et al, 2000 ; Schmidt et al, 2006 ; Krumhuber and Manstead, 2009 ; Hossain et al, 2020 ). In a spontaneous smile, the zygomatic major pulls the mouth corner upwards and the orbicularis oculi raises the cheek and so produces certain features, which can include a raised upper lip, stretched mouth, and displayed teeth.…”

Section: Introductionmentioning

confidence: 99%

Beyond Smiles: Static Expressions in Maxillary Protrusion and Associated Positivity

Chen

Jiang

et al. 2021

Front. Psychol.

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Smiles play an important role in social perception. However, it is unclear whether a similar role is played by static facial features associated with smiles (e.g., stretched mouth and visible teeth). In dental science, maxillary dental protrusions increase the baring of the teeth and thus produce partial facial features of a smile even when the individual is not choosing to smile, whereas mandibular dental protrusions do not. We conducted three experiments to assess whether individuals ascribe positive evaluations to these facial features, which are not genuine emotional expressions. In Experiment 1, participants viewed facial photographs of maxillary and mandibular protrusions and indicated the smiling and emotional status of the faces. The results showed that, while no difference was observed in participants’ perception of the presence of a smile across both types of dental protrusion, participants felt more positive to faces with maxillary than mandibular protrusions. In Experiment 2, participants completed an Implicit Association Test (IAT) test measuring implicit attitudes toward faces with maxillary vs. mandibular protrusions. The results showed that participants had more positive attitude toward faces with maxillary than mandibular protrusions. In Experiment 3, individuals with either maxillary or mandibular protrusions completed the same IAT test to assess whether any preference would be affected by in-group/out-group preferences. The results showed both groups had more positive attitudes toward faces with maxillary protrusion, indicating that this preference is independent of the group effect. These findings suggest that facial features associated with smiles are viewed positively in social situations. We discuss this in terms of the social-function account.

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“…In this paper, we explore the effects of four different physiological signals, Electrodermal Activity (EDA), Blood Volume Pulse (BVP), Skin Temperature (ST) and Pupil Dilation (PD) from subjects listening to music. All of these signals showed significant changes (reflecting the listener's reaction) to different stimuli [13,14,15]. Our previous study [16] had only explored the effects of EDA signals in differentiating different types of music based on genre and participants' subjective ratings.…”

Section: Introductionmentioning

confidence: 99%

Towards Effective Music Therapy for Mental Health Care Using Machine Learning Tools: Human Affective Reasoning and Music Genres

Rahman

Gedeon

Caldwell

et al. 2020

Journal of Artificial Intelligence and Soft Computing Research

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Music has the ability to evoke different emotions in people, which is reflected in their physiological signals. Advances in affective computing have introduced computational methods to analyse these signals and understand the relationship between music and emotion in greater detail. We analyse Electrodermal Activity (EDA), Blood Volume Pulse (BVP), Skin Temperature (ST) and Pupil Dilation (PD) collected from 24 participants while they listen to 12 pieces from 3 different genres of music. A set of 34 features were extracted from each signal and 6 different feature selection methods were applied to identify useful features. Empirical analysis shows that a neural network (NN) with a set of features extracted from the physiological signals can achieve 99.2% accuracy in differentiating among the 3 music genres. The model also reaches 98.5% accuracy in classification based on participants’ subjective rating of emotion. The paper also identifies some useful features to improve accuracy of the classification models. Furthermore, we introduce a new technique called ’Gingerbread Animation’ to visualise the physiological signals we record as a video, and to make these signals more comprehensible to the human eye, and also appropriate for computer vision techniques such as Convolutional Neural Networks (CNNs). Our results overall provide a strong motivation to investigate the relationship between physiological signals and music, which can lead to improvements in music therapy for mental health care and musicogenic epilepsy reduction (our long term goal).

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Using Temporal Features of Observers’ Physiological Measures to Distinguish Between Genuine and Fake Smiles

Cited by 16 publications

References 47 publications

Emotion Recognition Based on Skin Potential Signals with a Portable Wireless Device

Emotion Recognition Based on Skin Potential Signals with a Portable Wireless Device

Beyond Smiles: Static Expressions in Maxillary Protrusion and Associated Positivity

Towards Effective Music Therapy for Mental Health Care Using Machine Learning Tools: Human Affective Reasoning and Music Genres

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