14 September 2016 Sparse and low-rank feature extraction for the classification of target's tracking capability
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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.
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Behnood Rasti, Behnood Rasti, Karl Solvi Gudmundsson, Karl Solvi Gudmundsson, } "Sparse and low-rank feature extraction for the classification of target's tracking capability", Proc. SPIE 9970, Optics and Photonics for Information Processing X, 99701U (14 September 2016); doi: 10.1117/12.2240282; https://doi.org/10.1117/12.2240282
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