杠杆(统计)
计算机科学
对偶(语法数字)
表达式(计算机科学)
卷积神经网络
任务(项目管理)
人工智能
班级(哲学)
机器学习
模式识别(心理学)
文学类
艺术
经济
管理
程序设计语言
作者
Xuan Nie,Madhumita A. Takalkar,Mengyang Duan,Haimin Zhang,Min Xu
标识
DOI:10.1016/j.neucom.2020.10.082
摘要
Recognition of micro-expressions remains a topic of concern considering its brief span and low intensity. This issue is addressed through convolutional neural networks (CNNs) by developing multi-task learning (MTL) method to effectively leverage a side task: gender detection. A dual-stream multi-task framework called GEME is introduced that recognises micro-expressions by incorporating unique gender characteristics and subsequently improves the micro-expression recognition accuracy. This research aims to examine how gender differences influence the way micro-expressions are displayed. The current study proves that selecting relevant features of micro-expressions distinctive to the gender and added to the micro-expression features improves the micro-expression recognition accuracy. This network learns gender-specific features and micro-expression features and adds them together to learn the combination of shared and task-specific representations. A multi-class focal loss is used to mitigate the class imbalance issue by down-weighing the easy samples and concentrate more on misclassified samples. The Class-Balanced (CB) focal loss is also implemented for a better class balancing during Leave-One-Subject-Out (LOSO) validations where CB loss re-balances and re-weights the loss. The experimental results on three widely used databases demonstrate the improved performance of the proposed network and achieve comparable results with the state-of-the-art methods.
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