Time Unification on Local Binary Patterns Three Orthogonal Planes for Facial Expression Recognition
Résumé
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.
Mots clés
Feature extraction
Mathematical model
Videos
Interpolation
Histograms
Computational modeling
Face recognition
emotion recognition
face recognition
computer vision
geometry
image classification
learning (artificial intelligence)
neural nets
time unification method
microexpression classification
human gestures recognition
facial emotions
deep learning
human eye
machine learning
facial expression recognition
orthogonal planes
local binary patterns
LBP
Micro expression
optimisation
dissemin
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