Classifying and Visualizing Emotions with Emotional DAN

• Autorzy: Tautkutė Ivona , Trzciński Tomasz

Identyfikatory

DOI

10.3233/FI-2019-1832

• Języki publikacji: EN

Abstrakty

EN

Classification of human emotions remains an important and challenging task for many computer vision algorithms, especially in the era of humanoid robots which coexist with humans in their everyday life. Currently proposed methods for emotion recognition solve this task using multi-layered convolutional networks that do not explicitly infer any facial features in the classification phase. In this work, we postulate a fundamentally different approach to solve emotion recognition task that relies on incorporating facial landmarks as a part of the classification loss function. To that end, we extend a recently proposed Deep Alignment Network (DAN) with a term related to facial features. Thanks to this simple modification, our model called EmotionalDAN is able to outperform state-of-the-art emotion classification methods on two challenging benchmark dataset by up to 5%. Furthermore, we visualize image regions analyzed by the network when making a decision and the results indicate that our EmotionalDAN model is able to correctly identify facial landmarks responsible for expressing the emotions.

Słowa kluczowe

EN

machine learning; emotion recognition; facial expression recognition

• Wydawca: IOS Press
• Czasopismo: Fundamenta Informaticae
• Rocznik: 2019
• Tom: Vol. 168, nr 2-4
• Strony: 269--285
• Opis fizyczny: Bibliogr. 31 poz., fot., rys., tab.

Twórcy

autor

Tautkutė Ivona

• Polish-Japanese Academy of Information Technology, Tooploox, Warsaw, Poland

autor

Trzciński Tomasz

• Warsaw University of Technology, Tooploox, Warsaw, Poland

Bibliografia

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Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).