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

In order to improve trial efficiency and realize the automatic recognition of the micro-expressions of criminal suspects in the interrogation process, according to the characteristics of the human torso micro-expressions posture, A 6 points human torso model which is used to describe the human torso micro-expressions posture is proposed. An automatic recognition algorithm of the micro-expressions of human torso posture is designed. First, the original image of the human torso posture is converted to a local binary pattern image. The edge of the human torso is detected on the local binary pattern image by the canny operator. Then, the human torso posture characteristics are described and calculated by 6 points human skeleton model. Compared with the traditional 20 points human skeleton model, the key points of the left and right chest are added to the skele-ton model, and the key point of waist is expanded to about two points. The 6 points model is more suitable for describing the micro-expressions characteristics of human torso posture. Finally, the different postures are classified and recognized by support vector machine. Experimental results indicate that the recognition results of the 8 different human torso pose are uniform, and are all in the 83.1~92.5%. The average correct recognition rate of 8 different human torso posture is 87.9%. The average correct recognition rate of the recognition algorithm using the traditional 20 points human skeleton model is only 67.3%. Compared with the traditional 20 points human skeleton model, the 6 points human torso model can be more accurate and reasonable description of the characteristics of the human torso microexpressions posture. The algorithm can satisfy the requirements of human torso microexpressions posture automatic recognition, and effectively judge the different postures.

Automatic Recognition of the Micro-Expressions of Human Torso Posture in the Environment of Interrogation