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Children facial expressions detection with EEG from video

Dana Goghberg, Moran Hait and Sapphire Elimelech

Supervised by Gary Mataev, Yaron Honen, Ron Kimmel, Tzipi Horowitz Kraus, Michal Zivan

Abstract

Our project is the product of a collaboration between the Geometric Image Processing Lab (GIP) at the Computer Science department and the Educational Neuroimaging Center (ENIC) at the Technion. The goal of the project is to create an emotion recognition system based on facial expression recognition (FER) algorithm, which will be used by ENIC lab for analyzing children’s responses to various tasks, while being monitored by EEG/fMRI.
We track the children along a video using face recognition and image processing tools, and output their emotion using a classifier.
We used convolutional neural network to implement a new facial expression recognition system which is specifically designed for children, Although well studied on adults, only a few facial expression recognition studies have been done involving children, and consequently only a handful of small relevant datasets exist. This difficulty was added to the fact that some children’s emotions have similar representation, and are therefore difficult to differentiate.
We tested our chosen model using cross-validation and a real-time emotion detection from a video recorder. The results exceeded our expected goals.
We introduce a novel machine-learning system that detects facial expressions in children with 91% accuracy.
We are not aware of approaches yielding similar results in the literature.

Pictures
Project Children facial expressions detection with EEG from video Picture 1
Project Children facial expressions detection with EEG from video Picture 2
Project Report

Please, see project report.

Final Presentation

Please, see final presentation.

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