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Proceedings Paper

Sparse and low-rank feature extraction for the classification of target's tracking capability
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Paper Abstract

A feature extraction-based classification method is proposed in this paper for verifying the capability of human’s neck in target tracking. Here, the target moves in predefined trajectory patterns in three difficulty levels. Dataset used for each pattern is obtained from two groups of people, one with whiplash associated disorder (WAD) and asymptomatic group, who behave in both sincere and feign manner. The aim is to verify the WAD group from asymptomatic one and also to discriminate the sincere behavior from the feigned one. Sparse and low-rank feature extraction is proposed to extract the most informative feature from training samples and then each sample is classified into the group which has the highest correlation coefficient with. The classification results are improved by fusing the results of the three patterns.

Paper Details

Date Published: 14 September 2016
PDF: 7 pages
Proc. SPIE 9970, Optics and Photonics for Information Processing X, 99701U (14 September 2016); doi: 10.1117/12.2240282
Show Author Affiliations
Behnood Rasti, Univ. of Iceland (Iceland)
Karl Solvi Gudmundsson, Univ. of Iceland (Iceland)


Published in SPIE Proceedings Vol. 9970:
Optics and Photonics for Information Processing X
Khan M. Iftekharuddin; Abdul A. S. Awwal; Mireya García Vázquez; Andrés Márquez; Mohammad A. Matin, Editor(s)

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