A Novel Hybrid Pyramid Texture-Based Facial Expression Recognition

Fallah, Z.; Ebrahimpour-Komleh, Hossein; Mousavirad, Seyed Jalaleddin

doi:10.1109/ipria53572.2021.9483475

Cited by 1 publication

(1 citation statement)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…AnotherFERmethodispresentedusingtwodescriptors,whicharerespectivelytheOriented GradientHistogram(HOG)andtheLocalBinaryPatterns(LBP).Thetwodescriptorsareusedintwo differentways;locallyusingthefacialsub-regions(blocks)(l-HOGandl-LBP)andgloballyusing theentirefacialimage(g-HOG,g-LBP).Descriptorsgeneratedfromthepreviousbuildingblocks generatealargeamountofinformation.Forthis,theLocallyLineardimensionalityreductiontechnique isusedtoreducetheamountofinformation (Yaddaden,Adda,&Bouzouane,2021).Acombination ofPyramidalLocalBinaryPattern(PLBP)andPyramidalLocalPhaseQuantization(PLPQ)isused torecognizefacialexpressionsatalowresolution (Fallah,Ebrahimpour-Komleh,&Mousavirad, 2021).LBPisusedtoextractfeaturesinthespatialdomainandLPQisusedtoextractfeaturesinthe frequencydomain.ThecombinationofPLBPandPLPQinafeaturevectorcanprovideimportant informationthatcanbeexploitedbytheKNearestNeighbor.Theextractionofdiscriminatingfeatures canbeperformedusingmodifiedHOGandLBPfeaturedescriptors (Lakshmi,&Ponnusamy,2021). ThemodifiedHOGisconcatenatedwiththeLBPhistogramtogeneratearobustdescriptor.Then, DeepStackedAutoEncoders(DSAE)(Tewarietal.,2017)isusedtoreducethedimensionalityof concatenatedfeatures.Finally,amulti-classSVMisusedtorecognizefacialexpressions.…”

Section: Related Workmentioning

confidence: 99%

Hierarchical Spatial Pyramidal Feature Representation Learning for Facial Expression Recognition

Gafour

Berrabah²,

Djafri

et al. 2022

International Journal of Organizational and Collective Intelligence

View full text Add to dashboard Cite

As the recognition of facial expressions has attracted increasing attention due to its important applications in several fields (security, human-machine interaction, marketing, etc.), thanks to the detection of different possible expressions (happiness, sadness, etc.), this technology is gaining more attention. This article presents a method for the recognition of facial expressions by exploiting spatial information. In this method, the LBP descriptor is proposed to extract local characteristics, and a hierarchical spatial pyramid model is used to capture spatial information in an image. This method enriches the semantic description of visual information, which improves the recognition rate of facial expressions. The experimental analysis was carried out on the CK+ facial expression dataset, which includes seven categories of expressions—“happy,” “sad,” “surprise,” “contempt,” “fear,” “disgust,” “anger”—and it has been given state-of-the-art performance. The recognition results of the system are very satisfactory.

show abstract

Section: Related Workmentioning

confidence: 99%