Facial expression recognition based on region specific appearance and geometric features

Ghimire, Deepak; Jeong, Sunghwan; Yoon, Sun-Hong; Choi, Ju Hwan; Lee, Joonwhoan

doi:10.1109/icdim.2015.7381857

Cited by 13 publications

(1 citation statement)

References 16 publications

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“…Some applications can be referred [9] [10]. The combination of texture feature and geometric feature is also used [11][12] [13] and it was approved to achieved a better performance than only using a single feature.…”

Section: Introduction and Realted Workmentioning

confidence: 99%

In-the-wild facial expression recognition in extreme poses

Yang

Zhang

Zheng³

et al. 2018

Ninth International Conference on Graphic and Image Processing (ICGIP 2017)

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In the computer research area, facial expression recognition is a hot research problem. Recent years, the research has moved from the lab environment to in-the-wild circumstances. It is challenging, especially under extreme poses. But current expression detection systems are trying to avoid the pose effects and gain the general applicable ability. In this work, we solve the problem in the opposite approach. We consider the head poses and detect the expressions within special head poses. Our work includes two parts: detect the head pose and group it into one pre-defined head pose class; do facial expression recognize within each pose class. Our experiments show that the recognition results with pose class grouping are much better than that of direct recognition without considering poses. We combine the hand-crafted features, SIFT, LBP and geometric feature, with deep learning feature as the representation of the expressions. The handcrafted features are added into the deep learning framework along with the high level deep learning features. As a comparison, we implement SVM and random forest to as the prediction models. To train and test our methodology, we labeled the face dataset with 6 basic expressions.

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Section: Introduction and Realted Workmentioning

confidence: 99%