Convolutional neural networks pretrained on large face recognition datasets for emotion classification from video

2017/11/13 by Boris Knyazev, Knyazev, Boris, Roman Shvetsov +5 · 2 citations
Computer Science · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #cs.CV

paper · pdf · doi:10.48550/arxiv.1711.04598

4 pages

arxiv created 2017/11/13 · arxiv updated 2017/11/15

Abstract

In this paper we describe a solution to our entry for the emotion recognition challenge EmotiW 2017. We propose an ensemble of several models, which capture spatial and audio features from videos. Spatial features are captured by convolutional neural networks, pretrained on large face recognition datasets. We show that usage of strong industry-level face recognition networks increases the accuracy of emotion recognition. Using our ensemble we improve on the previous best result on the test set by about 1 %, achieving a 60.03 % classification accuracy without any use of visual temporal information.

Cited by

• Quality-Controlled Multimodal Emotion Recognition in Conversations with Identity-Based Transfer Learning and MAMBA Fusion
• Enhancing Ambiguous Dynamic Facial Expression Recognition with Soft Label-based Data Augmentation

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