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Kriging Predictor for Facial Emotion Recognition Using Numerical Proximities of Human Emotions
Volume 31, Issue 2 (2020), pp. 249–275
Pub. online: 2 June 2020      Type: Research Article      Open accessOpen Access
Received
1 January 2020
Accepted
1 May 2020
Published
2 June 2020

Abstract

Emotion recognition from facial expressions has gained much interest over the last few decades. In the literature, the common approach, used for facial emotion recognition (FER), consists of these steps: image pre-processing, face detection, facial feature extraction, and facial expression classification (recognition). We have developed a method for FER that is absolutely different from this common approach. Our method is based on the dimensional model of emotions as well as on using the kriging predictor of Fractional Brownian Vector Field. The classification problem, related to the recognition of facial emotions, is formulated and solved. The relationship of different emotions is estimated by expert psychologists by putting different emotions as the points on the plane. The goal is to get an estimate of a new picture emotion on the plane by kriging and determine which emotion, identified by psychologists, is the closest one. Seven basic emotions (Joy, Sadness, Surprise, Disgust, Anger, Fear, and Neutral) have been chosen. The accuracy of classification into seven classes has been obtained approximately 50%, if we make a decision on the basis of the closest basic emotion. It has been ascertained that the kriging predictor is suitable for facial emotion recognition in the case of small sets of pictures. More sophisticated classification strategies may increase the accuracy, when grouping of the basic emotions is applied.

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Biographies

Karbauskaitė Rasa
rasa.karbauskaite@mif.vu.lt

R. Karbauskaitė is a researcher of Cognitive Computing Group at Institute of Data Science and Digital Technologies of Vilnius University. She received a bachelor’s degree in mathematics and informatics (2003) and a master’s degree in informatics (2005) from Vilnius Pedagogical University, PhD in informatics from Vytautas Magnus University and Institute of Mathematics and Informatics (2010). Her research interests include multidimensional data visualization, estimation of the visualization quality, dimensionality reduction, estimation of the intrinsic dimensionality of high-dimensional data, facial emotion recognition, and data clustering.

Sakalauskas Leonidas
leonidas.sakalauskas@mif.vu.lt

L. Sakalauskas, habil. dr. (2000), prof. (2006), research interests: data mining, operations research, stochastic optimization, statistical modelling. He developed the stochastic optimization approach by Monte Carlo series and studied its convergence, developed the theory of vectorial fractal Brownian fields with implementation for surogate modelling, developed a concept of modelling and simulation of social-behavioural phenomena, etc. He has written more than 250 scientific publications, 70 of which are referenced in Clarivate Analytics DB, has supervised 15 PhD thesis, has organised more than 20 scientific conferences.

Dzemyda Gintautas
gintautas.dzemyda@mif.vu.lt

G. Dzemyda received the doctoral degree in technical sciences (PhD) in 1984, and he received the degree of Doctor Habilius in 1997 from Kaunas University of Technology. He was conferred the title of professor at Kaunas University of Technology (1998) and Vilnius University (2018). Recent employment is at Vilnius University, Institute of Data Science and Digital Technologies, as the director of the Institute, the head of Cognitive Computing Group, Professor and Principal Researcher. The research interests cover visualization of multidimensional data, optimization theory and applications, data mining, multiple criteria decision support, neural networks, image analysis. He is the author of more than 260 scientific publications, two monographs, five textbooks.


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Keywords
facial emotion recognition Fractional Brownian Vector Field kriging predictor dimensional models of emotions classifier

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