NDLI: Learning content-adaptive feature pooling for facial depression recognition in videos

Content Provider	IET Digital Library
Author	Zhou, Xiuzhuang Huang, Peng Liu, Haoming Niu, Sihua
Abstract	Recently, a deep representation of facial depression built on convolutional neural networks has shown impressive performance in video-based depression recognition. However, most existing approaches either fix the weights or using a certain heuristics to integrate the frame-level facial features, resulting in suboptimal feature aggregation in encoding the helpful while discarding noisy information in videos. To address this issue, the authors introduce the memory attention mechanism in a regression network to learn a deep discriminative depression representation, where the residual network module aims at learning frame-level deep feature, while the attention module acts as a pooling layer by adaptively learning the weights emphasising or suppressing face images with varying poses and imaging conditions. They empirically evaluate the proposed approach on a benchmark depression dataset, and the results demonstrate the superiority of their approach over the state-of-the-art alternatives.
Starting Page	648
Ending Page	650
Page Count	3
ISSN	00135194
Volume Number	55
e-ISSN	1350911X
Issue Number	Issue 11, May (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/el/55/11
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/el.2019.0443
Journal	Electronics Letters
Publisher Date	2019-04-29
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Attention Module Acts Benchmark Depression Dataset Computer Vision And Image Processing Technique Content-adaptive Feature Pooling Convolutional Neural Network Deep Discriminative Depression Representation Deep Representation Face Recognition Facial Depression Recognition Feature Extraction Frame-level Facial Feature Image Classification Image Recognition Image Representation Impressive Performance Knowledge Engineering Technique Learning Frame-level Deep Feature Learning in AI Memory Attention Mechanism Neural Computing Technique Neural Nets Noisy Information Pooling Layer Regression Network Residual Network Module Statistics Suboptimal Feature Aggregation Video-based Depression Recognition Videos
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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