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Proceedings Paper

Saccadic eyes recognition using 3-D shape data from a 3-D near infrared sensor
Author(s): Shenwen Guo; Jinshan Tang; Julia B. Parakkat; Kathleen M. Robinette
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Paper Abstract

Saccadic eyes are important human behaviors and have important applications in commercial and security fields. In this paper, we focus on saccadic eyes recognition from 3-D shape data acquired from a 3-D near infrared sensor. Two salient features, normal vectors of meshes and curvatures of surfaces, are extracted. The distributions of normal vectors and curvatures are computed to represent eye states. The support vector machine (SVM) is applied to classify eyes states into saccadic and non-saccadic eyes states. To verify the proposed method, we performed three groups of experiments using different strategies for samples selected from 300 3-D data, and present experimental results that demonstrate the effectiveness and robustness of the proposed algorithm.

Paper Details

Date Published: 8 May 2012
PDF: 7 pages
Proc. SPIE 8406, Mobile Multimedia/Image Processing, Security, and Applications 2012, 84060E (8 May 2012); doi: 10.1117/12.918414
Show Author Affiliations
Shenwen Guo, Alcorn State Univ. (United States)
Jinshan Tang, Michigan Technological Univ. (United States)
Julia B. Parakkat, Air Force Research Lab. (United States)
Kathleen M. Robinette, Air Force Research Lab. (United States)

Published in SPIE Proceedings Vol. 8406:
Mobile Multimedia/Image Processing, Security, and Applications 2012
Sos S. Agaian; Sabah A. Jassim; Eliza Yingzi Du, Editor(s)

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