|Table of Contents|

Research on Bimodal Facial Expression Based onImproved Random Forest Classifier(PDF)

《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

Issue:
2019年01期
Page:
82-
Research Field:
·人工智能算法与应用专栏·
Publishing date:

Info

Title:
Research on Bimodal Facial Expression Based onImproved Random Forest Classifier
Author(s):
Zhang Yuyu1; Ni Rongrong2; Yang Biao1
(1.School of Information Science and Engineering,Changzhou University,Changzhou 213164,China)(2.Changzhou Vocational Institute of Textile and Garment,Changzhou 213164,China)
Keywords:
face detection; facial expression recognition; RGBD facial images; feature selection based random forest; facial geometrical feature
PACS:
TP391
DOI:
10.3969/j.issn.1001-4616.2019.01.013
Abstract:
Facial expression recognition is an important way for machines to understand expression changes of human beings. Expression recognition using RGB facial images suffers from illumination changes and detail depicting in minor emotion changes. This work employ RGBD facial images to recognize 6 basic facial expressions(happiness,sadness,angry,disgust,fear and surprise). Depth images are used for robust face detection initially. Gray-scale facial images are employed to detect and to track 2D facial landmark points from the detected face region. Then,corresponding depth information is added into these points to construct depth facial geometrical feature which can recognize minor expression changes more effectively. Finally,a random forest classifier based on feature selection is designed to recognize different facial expressions. Results of comparative evaluations on benchmarking datasets verify the fact that our approach outperforms several state-of-the-art facial expression approaches,which use hand-crafted features,in recognizing six basic facial expressions. Meanwhile,our approach achieves almost similar performance comparing with the convolutional neural network based expression recognition algorithms.

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Last Update: 2019-03-30