Optimization of an individual re-identification modeling process using biometric features

We present results from the optimization of a reidentification 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. Multiple linear regression models are employed to estimate one set of features from the other. 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 reidentification rates than two other approaches that use all 43 gallery set features and information from all 2378 individuals.

[1]  Shishir K. Shah,et al.  A survey of approaches and trends in person re-identification , 2014, Image Vis. Comput..

[2]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[3]  Olga Mendoza-Schrock,et al.  Evolving robust gender classification features for CAESAR data , 2011, Proceedings of the 2011 IEEE National Aerospace and Electronics Conference (NAECON).

[4]  Riccardo Satta,et al.  Appearance Descriptors for Person Re-identification: a Comprehensive Review , 2013, ArXiv.

[5]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[6]  Olga Mendoza-Schrock,et al.  A limited comparative study of dimension reduction techniques on CAESAR , 2010, Proceedings of the IEEE 2010 National Aerospace & Electronics Conference.

[7]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[8]  Goldberg,et al.  Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.

[9]  Bir Bhanu,et al.  Individual recognition using gait energy image , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Phil Sallee,et al.  Training and feature-reduction techniques for human identification using anthropometry , 2010, 2010 Fourth IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS).

[11]  Afzal Godil,et al.  Human identification from body shape , 2003, Fourth International Conference on 3-D Digital Imaging and Modeling, 2003. 3DIM 2003. Proceedings..