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Erschienen in:
The Study of Improving the Accuracy of Convolutional Neural Networks in Face Recognition Tasks Kapitel lesen Erstes Kapitel lesen

2021 | OriginalPaper | Buchkapitel

MobileEmotiFace: Efficient Facial Image Representations in Video-Based Emotion Recognition on Mobile Devices

verfasst von : Polina Demochkina, Andrey V. Savchenko

Erschienen in: Pattern Recognition. ICPR International Workshops and Challenges

Verlag: Springer International Publishing

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Abstract

In this paper, we address the emotion classification problem in videos using a two-stage approach. At the first stage, deep features are extracted from facial regions detected in each video frame using a MobileNet-based image model. This network has been preliminarily trained to identify the age, gender, and identity of a person, and further fine-tuned on the AffectNet dataset to classify emotions in static images. At the second stage, the features of each frame are aggregated using multiple statistical functions (mean, standard deviation, min, max) into a single MobileEmotiFace descriptor of the whole video. The proposed approach is experimentally studied on the AFEW dataset from the EmotiW 2019 challenge. It was shown that our image mining technique leads to more accurate and much faster decision-making in video-based emotion recognition when compared to conventional feature extractors.

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Vorheriges Kapitel Automation of the Detection of Pathological Changes in the Morphometric Characteristics of the Human Eye Fundus Based on the Data of Optical Coherence Tomography Angiography
Nächstes Kapitel Basic Models of Descriptive Image Analysis
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Metadaten
Titel
MobileEmotiFace: Efficient Facial Image Representations in Video-Based Emotion Recognition on Mobile Devices
verfasst von
Polina Demochkina
Andrey V. Savchenko
Copyright-Jahr
2021
Buch
Pattern Recognition. ICPR International Workshops and Challenges

Print ISBN: 978-3-030-68820-2

Electronic ISBN: 978-3-030-68821-9

Copyright-Jahr: 2021

DOI
https://doi.org/10.1007/978-3-030-68821-9_25

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