Facial Micro-Expression Spotting and Recognition using Time Contrasted Feature with Visual Memory
This work addresses the problem of detecting subtle emotional cues in facial micro-expressions for applications like psychology or security, representing an incremental improvement over existing methods.
The paper tackles the challenge of spotting and recognizing facial micro-expressions, which are brief involuntary movements revealing concealed emotions, by proposing a joint spatial-temporal network with time-contrasted features and a memory module, achieving superior performance on the CASMEII dataset.
Facial micro-expressions are sudden involuntary minute muscle movements which reveal true emotions that people try to conceal. Spotting a micro-expression and recognizing it is a major challenge owing to its short duration and intensity. Many works pursued traditional and deep learning based approaches to solve this issue but compromised on learning low-level features and higher accuracy due to unavailability of datasets. This motivated us to propose a novel joint architecture of spatial and temporal network which extracts time-contrasted features from the feature maps to contrast out micro-expression from rapid muscle movements. The usage of time contrasted features greatly improved the spotting of micro-expression from inconspicuous facial movements. Also, we include a memory module to predict the class and intensity of the micro-expression across the temporal frames of the micro-expression clip. Our method achieves superior performance in comparison to other conventional approaches on CASMEII dataset.