Real time vision/sensor based features processing for efficient HCI employing canonical correlation analysis

Ehab H. El-Shazly, Moataz M. Abdelwahab, Atsushi Shimada, Rin-Ichiro Taniguchi

研究成果: ジャーナルへの寄稿記事

抄録

In this paper, a global algorithm for facial and gesture recognition is presented. The algorithm basically consists of three modules: features sensing and processing, dominant features selection and finally features matching. Depending on the type of data used (vision or sensor based), the proposed algorithm exploits multiple features employing 2DPCA that efficiently compact features’ descriptors maintain the spatial and temporal alignment of features’ components. Canonical Correlation Analysis (CCA) is employed to fuse different features from different descriptors or different performers. CCA also transforms training and testing features sets into new space where similar pairs become highly correlated pairs. Different experiments were conducted using well known data sets in addition to our newly collected data sets to verify the efficiency of the proposed algorithm. Excellent recognition accuracy, and fast performance are factors that promotes the proposed algorithm for real time implementation.

元の言語英語
ページ(範囲)187-195
ページ数9
ジャーナルJournal of Reliable Intelligent Environments
2
発行部数4
DOI
出版物ステータス出版済み - 12 1 2016

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Human computer interaction
Sensors
Processing
Gesture recognition
Electric fuses
Feature extraction
Testing
Experiments

All Science Journal Classification (ASJC) codes

  • Computer Science (miscellaneous)
  • Renewable Energy, Sustainability and the Environment

これを引用

Real time vision/sensor based features processing for efficient HCI employing canonical correlation analysis. / El-Shazly, Ehab H.; Abdelwahab, Moataz M.; Shimada, Atsushi; Taniguchi, Rin-Ichiro.

:: Journal of Reliable Intelligent Environments, 巻 2, 番号 4, 01.12.2016, p. 187-195.

研究成果: ジャーナルへの寄稿記事

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