Saccadic eyes recognition using 3-D shape data from a 3-D near infrared sensor

Shenwen Guo; Jinshan Tang; Julia B. Parakkat; Kathleen M. Robinette

doi:10.1117/12.918414

8 May 2012 Saccadic eyes recognition using 3-D shape data from a 3-D near infrared sensor

Shenwen Guo, Jinshan Tang, Julia B. Parakkat, Kathleen M. Robinette

Proceedings Volume 8406, Mobile Multimedia/Image Processing, Security, and Applications 2012; 84060E (2012) https://doi.org/10.1117/12.918414
Event: SPIE Defense, Security, and Sensing, 2012, Baltimore, Maryland, United States

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.

Citation Download Citation

Shenwen Guo, Jinshan Tang, Julia B. Parakkat, and Kathleen M. Robinette "Saccadic eyes recognition using 3-D shape data from a 3-D near infrared sensor", Proc. SPIE 8406, Mobile Multimedia/Image Processing, Security, and Applications 2012, 84060E (8 May 2012); https://doi.org/10.1117/12.918414

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