Facial micro-expression recognition using stochastic graph convolutional network and dual transferred learning

计算机科学 模式识别(心理学) 人工智能 卷积神经网络 图形 学习迁移 随机性 特征(语言学) 卷积(计算机科学) 机器学习 人工神经网络 理论计算机科学 数学 语言学 统计 哲学
作者
Hui Tang,Li Chai
出处
期刊:Neural Networks [Elsevier BV]
卷期号:178: 106421-106421 被引量:1
标识
DOI:10.1016/j.neunet.2024.106421
摘要

Micro-expression recognition (MER) has drawn increasing attention due to its wide application in lie detection, criminal detection and psychological consultation. However, the best recognition accuracy on recent public dataset is still low compared to the accuracy of macro-expression recognition. In this paper, we propose a novel graph convolution network (GCN) for MER achieving state-of-the-art accuracy. Different to existing GCN with fixed graph structure, we define a stochastic graph structure in which some neighbors are selected randomly. As shown by numerical examples, randomness enables better feature characterization while reducing computational complexity. The whole network consists of two branches, one is the spatial branch taking micro-expression images as input, the other is the temporal branch taking optical flow images as input. Because the micro-expression dataset does not have enough images for training the GCN, we employ the transfer learning mechanism. That is, different stochastic GCNs (SGCN) have been trained by the macro-expression dataset in the source network. Then the well-trained SGCNs are transferred to the target network. It is shown that our proposed method achieves the state-of-art performance on all four well-known datasets. This paper explores stochastic GCN and transfer learning with this random structure in the MER task, which is of great importance to improve the recognition performance.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
1秒前
科目三应助赫连紫采纳,获得10
2秒前
2秒前
littlestar发布了新的文献求助10
4秒前
4秒前
科研通AI5应助笑柳采纳,获得10
4秒前
Spinnin完成签到,获得积分10
5秒前
giao快查发布了新的文献求助30
8秒前
大个应助孙俪采纳,获得50
8秒前
花盛完成签到,获得积分10
10秒前
传奇3应助颖中竹子采纳,获得10
12秒前
Z160完成签到,获得积分10
15秒前
IP190237完成签到,获得积分0
18秒前
19秒前
龅牙苏完成签到,获得积分10
19秒前
科研通AI5应助超级气泡水采纳,获得10
19秒前
刘敏小七给刘敏小七的求助进行了留言
20秒前
漂亮水绿发布了新的文献求助20
21秒前
Hyy完成签到 ,获得积分10
24秒前
baibai发布了新的文献求助10
24秒前
25秒前
26秒前
白青发布了新的文献求助10
26秒前
滕擎发布了新的文献求助10
27秒前
荡南桥完成签到,获得积分10
28秒前
小亮哈哈完成签到,获得积分0
28秒前
文艺的初南完成签到 ,获得积分10
29秒前
感动听蓉发布了新的文献求助10
30秒前
大气石头完成签到,获得积分10
30秒前
30秒前
Ava应助JackyYOYO采纳,获得10
32秒前
三七发布了新的文献求助10
32秒前
阳离子应助老鱼吹浪采纳,获得10
33秒前
34秒前
段采萱发布了新的文献求助10
34秒前
拂晓梦彤发布了新的文献求助10
35秒前
manan发布了新的文献求助10
36秒前
高大凌寒应助踏实无敌采纳,获得200
37秒前
彭于彦祖应助白青采纳,获得10
39秒前
科研通AI5应助menyanyan采纳,获得10
40秒前
高分求助中
Les Mantodea de Guyane Insecta, Polyneoptera 2500
Technologies supporting mass customization of apparel: A pilot project 450
China—Art—Modernity: A Critical Introduction to Chinese Visual Expression from the Beginning of the Twentieth Century to the Present Day 430
A Field Guide to the Amphibians and Reptiles of Madagascar - Frank Glaw and Miguel Vences - 3rd Edition 400
China Gadabouts: New Frontiers of Humanitarian Nursing, 1941–51 400
The Healthy Socialist Life in Maoist China, 1949–1980 400
Walking a Tightrope: Memories of Wu Jieping, Personal Physician to China's Leaders 400
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 物理 生物化学 纳米技术 计算机科学 化学工程 内科学 复合材料 物理化学 电极 遗传学 量子力学 基因 冶金 催化作用
热门帖子
关注 科研通微信公众号,转发送积分 3789703
求助须知:如何正确求助?哪些是违规求助? 3334574
关于积分的说明 10270902
捐赠科研通 3051026
什么是DOI,文献DOI怎么找? 1674401
邀请新用户注册赠送积分活动 802553
科研通“疑难数据库(出版商)”最低求助积分说明 760777