Agent-based automated persuasion with adaptive concessions tuned by emotions

Wu, Jinghua; Zhang, Fujuan; Han, Jiali; Li, Yan; Sun, Yi

doi:10.1007/s12652-021-03089-w

Cited by 5 publications

(1 citation statement)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Fechner's law states that human perception is proportional to the logarithm of the intensity of the corresponding physical quantity, which is an improvement on Weber's law [ 44 ]. Fechner's law has been applied in areas related to human psychology and behavior [ 45 – 48 ]. Inspired by WLD, we imitate the Fechner's law and propose a feature descriptor, named Fechner multiscale local descriptor (FMLD).…”

Section: Introductionmentioning

confidence: 99%

A Fechner multiscale local descriptor for face recognition

Feng

Deng

et al. 2023

J Supercomput

View full text Add to dashboard Cite

Inspired by Fechner's law, we propose a Fechner multiscale local descriptor (FMLD) for feature extraction and face recognition. Fechner's law is a well-known law in psychology, which states that a human perception is proportional to the logarithm of the intensity of the corresponding significant differences physical quantity. FMLD uses the significant difference between pixels to simulate the pattern perception of human beings to the changes of surroundings. The first round of feature extraction is performed in two local domains of different sizes to capture the structural features of the facial images, resulting in four facial feature images. In the second round of feature extraction, two binary patterns are used to extract local features on the obtained magnitude and direction feature images, and four corresponding feature maps are output. Finally, all feature maps are fused to form an overall histogram feature. Different from the existing descriptors, the FMLD’s magnitude and direction features are not isolated. They are derived from the “perceived intensity”, thus there is a close relationship between them, which further facilitates the feature representation. In the experiments, we evaluated the performance of FMLD in multiple face databases and compared it with the leading edge approaches. The results show that the proposed FMLD performs well in recognizing images with illumination, pose, expression and occlusion changes. The results also indicate that the feature images produced by FMLD significantly improve the performance of convolutional neural network (CNN), and the combination of FMLD and CNN exhibits better performance than other advanced descriptors.

show abstract

Section: Introductionmentioning

confidence: 99%