Facial Expression

Lyakso, Elena E.; Frolova, Оlga; Matveev, Yuri

doi:10.4018/978-1-7998-6690-9.ch014

Cited by 10 publications

(8 citation statements)

References 38 publications

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“…Fewer studies have investigated the use of AFC for more dynamic and naturalistic observations, which is important to investigate as it allows for a better understanding of spontaneous emotional expressions in real-world contexts, capturing the intricacies of human interactions and reactions ( Lee et al, 2017 ). Additionally, whilst some works have used AFC to analyse parent facial expressions ( Karreman and Riem, 2020 ; Lyakso et al, 2021 ), there remains scope for deeper investigations, including those evaluating differences in gender.…”

Section: Discussionmentioning

confidence: 99%

“… Karreman and Riem (2020) used webcams to record mothers’ reactions to images of unknown infants, and used the software to analyse their facial expressions. Lyakso et al (2021) used FaceReader to quantify mother and infant facial expressions across various scenarios (e.g., mother-infant interactions, infant–infant interactions); all recordings in this work were made using a handheld video camera, and almost all interactions took place in naturalistic environments with uncontrolled lighting. The authors also excluded videos if the participants were not facing the camera ( Lyakso et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

“… Lyakso et al (2021) used FaceReader to quantify mother and infant facial expressions across various scenarios (e.g., mother-infant interactions, infant–infant interactions); all recordings in this work were made using a handheld video camera, and almost all interactions took place in naturalistic environments with uncontrolled lighting. The authors also excluded videos if the participants were not facing the camera ( Lyakso et al, 2021 ). Aside from these studies ( Karreman and Riem, 2020 ; Lyakso et al, 2021 ), we found no other literature evaluating maternal expressions using FaceReader, and we found no work either using FaceReader to evaluate paternal expressions, or drawing comparisons based on parent gender.…”

Section: Introductionmentioning

confidence: 99%

“…The authors also excluded videos if the participants were not facing the camera ( Lyakso et al, 2021 ). Aside from these studies ( Karreman and Riem, 2020 ; Lyakso et al, 2021 ), we found no other literature evaluating maternal expressions using FaceReader, and we found no work either using FaceReader to evaluate paternal expressions, or drawing comparisons based on parent gender.…”

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: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Burgess,

Culpin,

Costantini

et al. 2023

Front. Psychol.

View full text Add to dashboard Cite

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“…Parallel with the recording of the speech, the child’s behavior and mimic expression were recorded using a video camera “SONY HDR-CX560E”. Video recording is carried out in studies of the emotions’ manifestation in speech and facial expressions [ 32 , 33 ] and, along with the recording protocol, is used by experts when annotating speech material that reflects different emotions. The recording was carried out in rooms without special soundproofing.…”

Section: Methodsmentioning

confidence: 99%

The children’s emotional speech recognition by adults: Cross-cultural study on Russian and Tamil language

et al. 2023

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The current study investigated the features of cross-cultural recognition of four basic emotions “joy–neutral (calm state)–sad–anger” in the spontaneous and acting speech of Indian and Russian children aged 8–12 years across Russian and Tamil languages. The research tasks were to examine the ability of Russian and Indian experts to recognize the state of Russian and Indian children by their speech, determine the acoustic features of correctly recognized speech samples, and specify the influence of the expert’s language on the cross-cultural recognition of the emotional states of children. The study includes a perceptual auditory study by listeners and instrumental spectrographic analysis of child speech. Different accuracy and agreement between Russian and Indian experts were shown in recognizing the emotional states of Indian and Russian children by their speech, with more accurate recognition of the emotional state of children in their native language, in acting speech vs spontaneous speech. Both groups of experts recognize the state of anger via acting speech with the high agreement. The difference between the groups of experts was in the definition of joy, sadness, and neutral states depending on the test material with a different agreement. Speech signals with emphasized differences in acoustic patterns were more accurately classified by experts as belonging to emotions of different activation. The data showed that, despite the universality of basic emotions, on the one hand, the cultural environment affects their expression and perception, on the other hand, there are universal non-linguistic acoustic features of the voice that allow us to identify emotions via speech.

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Recognition of the Emotional State of Children with Down Syndrome by Video, Audio and Text Modalities: Human and Automatic

Lyakso

Frolova

Matveev

et al. 2022

Speech and Computer

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Facial Expression

Cited by 10 publications

References 38 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

The children’s emotional speech recognition by adults: Cross-cultural study on Russian and Tamil language

Recognition of the Emotional State of Children with Down Syndrome by Video, Audio and Text Modalities: Human and Automatic

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