Applying manifold learning techniques to the CAESAR database

Olga Mendoza-Schrock; James Patrick; Gregory Arnold; Matthew Ferrara

doi:10.1117/12.851722

15 April 2010 Applying manifold learning techniques to the CAESAR database

Olga Mendoza-Schrock, James Patrick, Gregory Arnold, Matthew Ferrara

Proceedings Volume 7704, Evolutionary and Bio-Inspired Computation: Theory and Applications IV; 77040O (2010) https://doi.org/10.1117/12.851722
Event: SPIE Defense, Security, and Sensing, 2010, Orlando, Florida, United States

Abstract

Understanding and organizing data is the first step toward exploiting sensor phenomenology for dismount tracking. What image features are good for distinguishing people and what measurements, or combination of measurements, can be used to classify the dataset by demographics including gender, age, and race? A particular technique, Diffusion Maps, has demonstrated the potential to extract features that intuitively make sense [1]. We want to develop an understanding of this tool by validating existing results on the Civilian American and European Surface Anthropometry Resource (CAESAR) database. This database, provided by the Air Force Research Laboratory (AFRL) Human Effectiveness Directorate and SAE International, is a rich dataset which includes 40 traditional, anthropometric measurements of 4400 human subjects. If we could specifically measure the defining features for classification, from this database, then the future question will then be to determine a subset of these features that can be measured from imagery. This paper briefly describes the Diffusion Map technique, shows potential for dimension reduction of the CAESAR database, and describes interesting problems to be further explored.

Citation Download Citation

Olga Mendoza-Schrock, James Patrick, Gregory Arnold, and Matthew Ferrara "Applying manifold learning techniques to the CAESAR database", Proc. SPIE 7704, Evolutionary and Bio-Inspired Computation: Theory and Applications IV, 77040O (15 April 2010); https://doi.org/10.1117/12.851722

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