6533b832fe1ef96bd129a4c8
RESEARCH PRODUCT
Time Unification on Local Binary Patterns Three Orthogonal Planes for Facial Expression Recognition
Fan YangDominique GinhacCyrille MigniotReda Belaichesubject
human eyeHistogramsgeometryUnificationComputer scienceLocal binary patternsoptimisationFeature extraction02 engineering and technologyhuman gestures recognitionFacial recognition systemcomputer visionVideos[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]time unification method03 medical and health sciences0302 clinical medicineMathematical modelLBPemotion recognition0202 electrical engineering electronic engineering information engineeringfacial emotionsfacial expression recognitionlocal binary patternsFace recognitionContextual image classificationArtificial neural networkbusiness.industryDeep learningdeep learning[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]Pattern recognitionComputational modelingmicroexpression classificationInterpolationorthogonal planesneural netsmachine learning[INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV]Micro expressionFeature extraction020201 artificial intelligence & image processinglearning (artificial intelligence)Artificial intelligencebusiness030217 neurology & neurosurgeryGestureimage classificationdescription
International audience; Machine learning has known a tremendous growth within the last years, and lately, thanks to that, some computer vision algorithms started to access what is difficult or even impossible to perceive by the human eye. While deep learning based computer vision algorithms have made themselves more and more present in the recent years, more classical feature extraction methods, such as the ones based on Local Binary Patterns (LBP), still present a non negligible interest, especially when dealing with small datasets. Furthermore, this operator has proven to be quite useful for facial emotions and human gestures recognition in general. Micro-Expression (ME) classification is among the applications of computer vision that heavily relied on hand crafted features in the past years. LBP Three Orthogonal Planes (LBP_TOP) is one of the most used hand crafted features extractor in the scientific literature to tackle the problem of ME classification. In this paper we present a time unification method that provides better results than the classical LBP_TOP while also drastically reducing the calculations required for feature extraction.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2019-11-26 |