Facial analysis aided human gesture recognition for Human Computer Interaction

Dan Luo*, Hua Gao, Hazum Kemal Ekenel, Jun Ohya

*この研究の対応する著者

研究成果: Conference contribution

抄録

Human gesture recognition systems are natural to use for achieving intelligent Human Computer Interaction (HCI). These systems should memorize the specific user and enable the user to gesture naturally without wearing special devices. Extracting different components of visual actions from human gestures, such as shape and motion of hand- facial expression and torso, is the key tasks in gesture recognition. So far, in the field of gesture n-cognition. most of the previous work have focused only on hand motion features and required the user lo wear special devices. In this paper, we present, an appearance-based multimodal gesture recognition framework, which combines the different modalities of features such as face identity, facial expression and hand motions which have been extracted from the image frames captured directly by a web camera. We refer 12 classes of human gestures with facial expression including neutral, negative (e.g. "angry") and positive (e.g. "excited") meanings from American Sign Language. A condensation-based algorithm is adopted for classification. We collected a data set with three recording sessions and conducted experiments with different combination techniques. Experimental results showed that the performance of hand gesture recognition is improved by adding facial analysis.

本文言語English
ホスト出版物のタイトルProceedings of the 12th IAPR Conference on Machine Vision Applications, MVA 2011
ページ446-449
ページ数4
出版ステータスPublished - 2011
イベント12th IAPR Conference on Machine Vision Applications, MVA 2011 - Nara, Japan
継続期間: 2011 6 132011 6 15

出版物シリーズ

名前Proceedings of the 12th IAPR Conference on Machine Vision Applications, MVA 2011

Conference

Conference12th IAPR Conference on Machine Vision Applications, MVA 2011
国/地域Japan
CityNara
Period11/6/1311/6/15

ASJC Scopus subject areas

  • コンピュータ ビジョンおよびパターン認識

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