Paper Info
Title
G2-Ver: Geometry Guided Model Ensemble For Video-Based Facial Expression Recognition
Abstract
This paper addresses the problem of automatic facial expression recognition in videos, where the goal is to predict discrete emotion labels best describing the emotions expressed in short video clips. Building on a pre-trained convolutional neural network ( CNN) model dedicated to analyzing the video frames and LSTM network designed to process the trajectories of the facial landmarks, this paper investigates several novel directions. First of all, improved face descriptors based on 2D CNNs and facial landmarks are proposed. Second, the paper investigates fusion methods of the features temporally, including a novel hierarchical recurrent neural network combining facial landmark trajectories over time. In addition, we propose a modification to state-of-the-art expression recognition architectures to adapt them to video processing in a simple way. In both ensemble approaches, the temporal information is integrated. Comparative experiments on publicly available video-based facial expression recognition datasets verified that the proposed framework outperforms state-of-the-art methods. Moreover, we introduce a near-infrared video dataset containing facial expressions from subjects driving their cars, which are recorded in real world conditions.
Year
DOI
Venue
2019
10.1109/FG.2019.8756600
2019 14TH IEEE INTERNATIONAL CONFERENCE ON AUTOMATIC FACE AND GESTURE RECOGNITION (FG 2019)
Field
DocType
ISSN
Video processing,Facial expression recognition,Pattern recognition,Convolutional neural network,Computer science,Recurrent neural network,Facial expression,Artificial intelligence,Landmark
Conference
2326-5396
Citations 
PageRank 
References 
0
0.34
0
Authors
7
Name
Order
Citations
PageRank
Tanguy Albrici100.34
Mandana Fasounaki200.34
Saleh Bagher Salimi300.34
Guillaume Vray400.34
Behzad Bozorgtabar500.34
Hazim Kemal Ekenel637744.16
Jean-Philippe Thiran722.77