Open AccessImproved Two‐Phase Framework for Facial Emotion Recognition
Author(s) -
Yoon Hyunjin,
Park Sangwook,
Lee Yongkwi,
Han Mikyong,
Jang JongHyun
Publication year - 2015
Publication title -
etri journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.295
H-Index - 46
eISSN - 2233-7326
pISSN - 1225-6463
DOI - 10.4218/etrij.15.0114.0523
Subject(s) - emotion recognition , emotion detection , computer science , detector , facial expression , artificial intelligence , scheme (mathematics) , pattern recognition (psychology) , face (sociological concept) , key (lock) , speech recognition , emotion classification , action (physics) , facial recognition system , computer vision , mathematics , mathematical analysis , telecommunications , social science , physics , computer security , quantum mechanics , sociology
Automatic emotion recognition based on facial cues, such as facial action units (AUs), has received huge attention in the last decade due to its wide variety of applications. Current computer‐based automated two‐phase facial emotion recognition procedures first detect AUs from input images and then infer target emotions from the detected AUs. However, more robust AU detection and AU‐to‐emotion mapping methods are required to deal with the error accumulation problem inherent in the multiphase scheme. Motivated by our key observation that a single AU detector does not perform equally well for all AUs, we propose a novel two‐phase facial emotion recognition framework, where the presence of AUs is detected by group decisions of multiple AU detectors and a target emotion is inferred from the combined AU detection decisions. Our emotion recognition framework consists of three major components — multiple AU detection, AU detection fusion, and AU‐to‐emotion mapping. The experimental results on two real‐world face databases demonstrate an improved performance over the previous two‐phase method using a single AU detector in terms of both AU detection accuracy and correct emotion recognition rate.