Open AccessLearning Effective Video Features for Facial Expression Recognition via Hybrid Deep Learning
Author(s) -
Akshay Kumar,
G. Divya
Publication year - 2020
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.e6767.018520
Subject(s) - artificial intelligence , computer science , deep learning , discriminative model , convolutional neural network , facial expression recognition , deep belief network , pattern recognition (psychology) , facial expression , expression (computer science) , computer vision , facial recognition system , programming language
Facial Expression Recognition is one of the recent trends to detect human expression in streaming video sequences. To identify emotions of video like sad, happy or angry. In this paper, the proposed method employs two individual deep convolution neural networks (CNNs), including a permanent CNN processing of static facial images and a temporary CN network processing of optical flow images, to separately learn high-level spatial and temporal characteristics on the separated video segments. Such two CNNs are fine tuned from a pre-trained CNN model to target video facial expression datasets. The spatial and temporal characteristics obtained at the segment level are then incorporated into a deep fusion network built with a model of deep belief network (DBN). This deep fusion network is used to learn spatiotemporal discriminative features together