Detecting Micro-expression Intensity Changes from Videos Based on Hybrid Deep CNN
Facial micro-expressions,which usually last only for a fraction of a second,are challenging to detect by the human eye or machine.They are useful for understanding the genuine emotional state of a human face,and have various applications in education,medical,surveillance and legal sectors.Existing works on micro-expressions are focused on binary classification of the micro-expressions.However,detecting the micro-expression intensity changes over the spanning time,i.e.,the micro-expression profiling,is not addressed in the literature.In this paper,we present a novel deep Convolutional Neural Network(CNN)based hybrid framework for micro-expression intensity change detection together with an image pre-processing technique.The two components of our hybrid framework,namely a micro-expression stage classifier,and an intensity estimator,are designed using a 3D and 2D shallow deep CNNs respectively.Moreover,we propose a fusion mechanism to improve the micro-expression intensity classification accuracy.Evaluation using the recent benchmark micro-expression datasets; CASME,CASME Ⅱ and SAMM,demonstrates that our hybrid framework can accurately classify the various intensity levels of each micro-expression.Further,comparison with the state-of-the-art methods reveals the superiority of our hybrid approach in classifying the micro-expressions accurately.
Micro-expression intensity Convolutional Neural Networks Hybrid framework Fusion mechanism
Selvarajah Thuseethan Sutharshan Rajasegarar John Yearwood
Deakin University,Geelong,VIC 3220,Australia
国际会议
澳门
英文
387-399
2019-04-14（万方平台首次上网日期，不代表论文的发表时间）