Full-motion video analysis for improved gender classification

Jeffrey B. Flora; Darrell F. Lochtefeld; Khan M. Iftekharuddin

doi:10.1117/12.2053390

19 June 2014 Full-motion video analysis for improved gender classification

Jeffrey B. Flora, Darrell F. Lochtefeld, Khan M. Iftekharuddin

Proceedings Volume 9089, Geospatial InfoFusion and Video Analytics IV; and Motion Imagery for ISR and Situational Awareness II; 90890Q (2014) https://doi.org/10.1117/12.2053390
Event: SPIE Defense + Security, 2014, Baltimore, MD, United States

Abstract

The ability of computer systems to perform gender classification using the dynamic motion of the human subject has important applications in medicine, human factors, and human-computer interface systems. Previous works in motion analysis have used data from sensors (including gyroscopes, accelerometers, and force plates), radar signatures, and video. However, full-motion video, motion capture, range data provides a higher resolution time and spatial dataset for the analysis of dynamic motion. Works using motion capture data have been limited by small datasets in a controlled environment. In this paper, we explore machine learning techniques to a new dataset that has a larger number of subjects. Additionally, these subjects move unrestricted through a capture volume, representing a more realistic, less controlled environment. We conclude that existing linear classification methods are insufficient for the gender classification for larger dataset captured in relatively uncontrolled environment. A method based on a nonlinear support vector machine classifier is proposed to obtain gender classification for the larger dataset. In experimental testing with a dataset consisting of 98 trials (49 subjects, 2 trials per subject), classification rates using leave-one-out cross-validation are improved from 73% using linear discriminant analysis to 88% using the nonlinear support vector machine classifier.

Citation Download Citation

Jeffrey B. Flora, Darrell F. Lochtefeld, and Khan M. Iftekharuddin "Full-motion video analysis for improved gender classification", Proc. SPIE 9089, Geospatial InfoFusion and Video Analytics IV; and Motion Imagery for ISR and Situational Awareness II, 90890Q (19 June 2014); https://doi.org/10.1117/12.2053390

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