We present results from the optimization of a re-identification process using two sets of biometric data obtained from the Civilian American and European Surface Anthropometry Resource Project (CAESAR) database. The datasets contain real measurements of features for 2378 individuals in a standing (43 features) and seated (16 features) position. A genetic algorithm (GA) was used to search a large combinatorial space where different features are available between the probe (seated) and gallery (standing) datasets. Results show that optimized model predictions obtained using less than half of the 43 gallery features and data from roughly 16% of the individuals available produce better re-identification rates than two other approaches that use all the information available.
Revised: January 16, 2017 |
Published: September 24, 2014
Citation
Heredia-Langner A., B.G. Amidan, S. Matzner, and K.H. Jarman. 2014.Optimization of an individual re-identification modeling process using biometric features. In Proceedings of the International Conference on Data Mining (DMIN 2014), July 21-24, 2014, Las Vegas, Nevada, edited by R Stahlbock, GM Weiss, M Abou-Nasr and H Arabnia. Athens, Georgia:CSREA Press.PNNL-SA-102023.