English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Micro-expression is a subtle and involuntary facial expression that may reveal the hidden emotion of human beings. Spotting micro-expression means to locate the moment when the micro-expression happens, which is a primary step for micro-expression recognition. Previous work in micro-expression spotting focus on spotting micro-expression from short video, and with hand-crafted features. In this paper, we present a methodology for spotting micro-expression from long videos. Specifically, a new convolutional neural network named spotting micro-expression convolutional network was designed for extracting features from video clips, which is the first time that deep learning is used in micro-expression spotting. Then, a feature matrix processing method was proposed for spotting the apex frame from long video, which uses a sliding window and takes the characteristics of micro-expression into account to search the apex frame. Experimental results demonstrate that the proposed method can achieve a better performance than the existing state-of-art methods.

SMEConvNet: A Convolutional Neural Network for Spotting Spontaneous Facial Micro-Expression From Long Videos