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

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...

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Bibliographic Details
Published in:Journal of reliable intelligent environments 2016-12, Vol.2 (4), p.187-195
Main Authors: El-Shazly, Ehab H., Abdelwahab, Moataz M., Shimada, Atsushi, Taniguchi, Rin-ichiro
Format: Article
Language:English
Subjects:
Artificial Intelligence
Computer Science
Correlation analysis
Health Informatics
Original Article
Performance and Reliability
Simulation and Modeling
Software Engineering/Programming and Operating Systems
User Interfaces and Human Computer Interaction
Vision systems
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Summary: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.
ISSN:2199-4668
2199-4676
DOI:10.1007/s40860-016-0028-4