Appearance-Based Smile Intensity Estimation by Cascaded Support Vector Machines

Shimada, Keiji; Matsukawa, Tetsu; Noguchi, Yoshihiro; Kurita, Takio

doi:10.1007/978-3-642-22822-3_28

Cited by 8 publications

(7 citation statements)

References 4 publications

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“…The rates offered by this author are remarkable and similar to those offered by other authors like Shimada 30 . He proposed a method that combines a new LBP based approach and local intensity histograms.…”

Section: Discussionsupporting

confidence: 87%

“…These authors contend that the training set plays almost as important role as the detection method applied, especially in terms of variability and size, and asserts that a normal training phase may handle thousands of images. More recently, works using public databases have been proposed by Huang 17 , Yadappanavar 39 and Shimada 30 . These works as well as two commercial systems are discussed in section 7.…”

Section: State Of the Artmentioning

confidence: 99%

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An Evolutive Approach for Smile Recognition in Video Sequences

Freire-Obregón

Castrillón-Santana

2015

Int. J. Patt. Recogn. Artif. Intell.

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Facial expression recognition is one of the most challenging research areas in the image recognition field and has been actively studied since the 70's. For instance, smile recognition has been studied due to the fact that it is considered an important facial expression in human communication, it is therefore likely useful for human-machine interaction. Moreover, if a smile can be detected and also its intensity estimated, it will raise the possibility of new applications in the future. We are talking about quantifying the emotion at low computation cost and high accuracy. For this aim, we have used a new support vector machine (SVM) based approach that integrates a weighted combination of local binary patterns (LBPs) and principal component analysis (PCA) based approaches. Furthermore, we construct this smile detector considering the evolution of the emotion along its natural life cycle. As a consequence, we achieved both low computation cost and high performance with video sequences.

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Section: Discussionsupporting

confidence: 87%

Section: State Of the Artmentioning

confidence: 99%

An Evolutive Approach for Smile Recognition in Video Sequences

Freire-Obregón

Castrillón-Santana

2015

Int. J. Patt. Recogn. Artif. Intell.

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“…Since Bartlett et al (2003), many studies have used classifier decision values to estimate expression intensity (Bartlett et al, 2006a,b, Littlewort et al, 2006, Reilly et al, 2006, Koelstra and Pantic, 2008, Whitehill et al, 2009, Yang et al, 2009, Savran et al, 2011, Shimada et al, 2011. However, only a few of them have quantitatively evaluated their performance by comparing their estimations to manual (i.e., "ground truth") coding.…”

Section: Previous Workmentioning

confidence: 99%

Untitled

2018

Preprint

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Both the occurrence and intensity of facial expressions are critical to what the face reveals. While much progress has been made towards the automatic detection of facial expression occurrence, controversy exists about how to estimate expression intensity. The most straightforward approach is to train multiclass or regression models using intensity ground truth. However, collecting intensity ground truth is even more time consuming and expensive than collecting binary ground truth. As a shortcut, some researchers have proposed using the decision values of binary-trained maximum margin classifiers as a proxy for expression intensity. We provide empirical evidence that this heuristic is flawed in practice as well as in theory. Unfortunately, there are no shortcuts when it comes to estimating smile intensity: researchers must take the time to collect and train on intensity ground truth. However, if they do so, high reliability with expert human coders can be achieved. Intensity-trained multiclass and regression models outperformed binary-trained classifier decision values on smile intensity estimation across multiple databases and methods for feature extraction and dimensionality reduction. Multiclass models even outperformed binarytrained classifiers on smile occurrence detection.

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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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Appearance-Based Smile Intensity Estimation by Cascaded Support Vector Machines

Cited by 8 publications

References 4 publications

An Evolutive Approach for Smile Recognition in Video Sequences

An Evolutive Approach for Smile Recognition in Video Sequences

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

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