Smile intensity recognition in real time videos: fuzzy system approach

Vinola, C; Devi, K. Vimala

doi:10.1007/s11042-018-6890-8

Cited by 6 publications

(6 citation statements)

References 33 publications

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“…For instance, Bahreini et al [17] calculated 54 cosine values for six basic emotion classification work. Vinola and Vimala Devi [24] calculated five Euclidean distances between ten landmark points for smile intensity work only. This proposed fusionbased facial emotion intensity classifier work overcomes all the above-mentioned limitations by generating a classifier with two modules.…”

Section: Discussion and Future Workmentioning

confidence: 99%

“…Euclidean distance cannot use as such because the estimated value varies from image to image depends upon the location of face and area of face segment. To standardize the ED we exerted normalized Euclidean distance as described by Vinola and Vimala Devi [24] in their smile intensity work. Figure 5 shows the height and width of the face and 68 landmark points and Fig.…”

Section: Feature Values Estimation/estimation Of Area and Tangentmentioning

confidence: 99%

“…In this work, facial features were represented by 6 angles and achieved a recognition accuracy of 72%. Vinola and Vimala Devi [24] generated fuzzy-based smile intensity work and considered the Euclidean distance concept between landmark points. This work achieved a recognition rate of 86.54%.…”

Section: Introductionmentioning

confidence: 99%

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Facial Emotion Intensity: A Fusion Way

Pandey

Kumar

2022

SN COMPUT. SCI.

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Many scientific works have been conducted for developing the Emotion intensity recognition system. But developing a system that is capable to estimate small to peak intensity levels with less complexity is still challenging. Therefore, we propose an effective facial emotion intensity classifier by fusion of the pre-trained deep architecture and fuzzy inference system. The pre-trained architecture VGG16 is used for basic emotion classification and it predicts emotion class with the class index value. By class index value, images are sent to the corresponding Fuzzy inference system for estimating the intensity level of detected emotion. This fusion model effectively identifies the facial emotions (happy, sad, surprise, and angry) and also predict the 13 categories of emotion intensity. This fusion model got 83% accuracy on a combined dataset (FER 2013, CK + and KDEF). The performance and findings of this proposed work are further compared with state-of-the-art models.

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Section: Discussion and Future Workmentioning

confidence: 99%

Section: Feature Values Estimation/estimation Of Area and Tangentmentioning

confidence: 99%

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Facial Emotion Intensity: A Fusion Way

Pandey

Kumar

2022

SN COMPUT. SCI.

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“…Application [4] Genetic Algorithm EEG signal processing to extract mixed features [5] Cascade Classifier and Tensor Processing Driver's eye recognition and fatigue monitoring [6] Fuzzy Logic approach Smile intensity recognition [7] Convolutional Neural Network Recognition of facial expression [8] Image mining Intelligent drowsy eye detection [9] Blind Image Separation Mixing and Estimating Sample Images [10] Convolutional Neural Network…”

Section: Author(s) Algorithmmentioning

confidence: 99%

Fatigue Detection among Operators in Industry Based on Euclidean Distance Computation Using Python Software

2020

IJETER

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Machine -learning is one of popular technique suitable for adaptation in Industrial Revolution 4.0 (IR4). There is a dire problem whereby increasing occupational accident especially in the manufacturing sectors. The root cause of these accidents are because of fatigue while performing repetitive task in production line. To solve this problem, a research was conducted in developing fatigue detection algorithm. The software used for this algorithm development is python software since this software is an open source software. Euclidean distance computation is utilized in this algorithm in determining the eyes and mouth aspect ratio. The eyes and mouth aspect ratio were set 0.28 and 0.60 respectively. If the eyes aspect ratio is below than 0.28, the output obtained is eyes closed. If mouth aspect ratio is higher than 0.60, than the operator is yawning and fatigue alert will appear notify the line leader in manufacturing plant.

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“…Automatic smile detection has been already addressed considering different issues and exploring various dimensions (see for example (Whitehill, Littlewort, Fasel, Bartlett, & Movellan, 2009)). The proposed solutions vary indeed whether one wants to detect the presence or the absence of smile (An, Yang, & Bhanu, 2015;Chen, Ou, Chi, & Fu, 2017;Guo, Polania, & Barner, 2018;Shan, 2012;Zhang, Huang, Wu, & Wang, 2015) or rather one wants to estimate smile intensity (Bartlett, Littlewort, Braathen, Sejnowski, & Movellan, 2003;Bartlett et al, 2006;Girard, Cohn, & De la Torre, 2015;Jiang, Coskun, Badokhon, Liu, & Huang, 2019;Shimada, Matsukawa, Noguchi, & Kurita, 2010;Vinola & Vimala Devi, 2019). The methods applied also change if one is interested in classifying single face image (An et al, 2015;Chen et al, 2017;Guo et al, 2018;Jiang et al, 2019;Shan, 2012;Shimada et al, 2010;Zhang et al, 2015) rather than proposing a dynamical annotation of a video recording (Freire-Obregón & Castrillón-Santana, 2015).…”

Section: Introductionmentioning

confidence: 99%

Automatic tool to annotate smile intensities in conversational face-to-face interactions

Rauzy,

Amoyal

2022

GEST

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This study presents an automatic tool that allows to trace smile intensities along a video record of conversational face-to-face interactions. The processed output proposes a sequence of adjusted time intervals labeled following the Smiling Intensity Scale (Gironzetti, Attardo, and Pickering, 2016), a 5 levels scale varying from neutral facial expression to laughing smile. The underlying statistical model of this tool is trained on a manually annotated corpus of conversations featuring spontaneous facial expressions. This model will be detailed in this study. This tool can be used with benefits for annotating smile in interactions. The results are twofold. First, the evaluation reveals an observed agreement of 68% between manual and automatic annotations. Second, manually correcting the labels and interval boundaries of the automatic outputs reduces by a factor 10 the annotation time as compared with the time spent for manually annotating smile intensities without pretreatment. Our annotation engine makes use of the state-of-the-art toolbox OpenFace for tracking the face and for measuring the intensities of the facial Action Units of interest all along the video. The documentation and the scripts of our tool, the SMAD software, are available to download at the HMAD open source project URL page https://github.com/srauzy/HMAD (last access 31 July 2023).

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Smile intensity recognition in real time videos: fuzzy system approach

Cited by 6 publications

References 33 publications

Facial Emotion Intensity: A Fusion Way

Facial Emotion Intensity: A Fusion Way

Fatigue Detection among Operators in Industry Based on Euclidean Distance Computation Using Python Software

Automatic tool to annotate smile intensities in conversational face-to-face interactions

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