Optimization of an individual re-identification modeling process using biometric features
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.
- Research Organization:
- Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- AC05-76RL01830
- OSTI ID:
- 1339932
- Report Number(s):
- PNNL-SA-102023
- Resource Relation:
- Conference: Proceedings of the International Conference on Data Mining (DMIN 2014), July 21-24, 2014, Las Vegas, Nevada
- Country of Publication:
- United States
- Language:
- English
Similar Records
Temporal Stability of Visual Search-Driven Biometrics
Geometry and Gesture-Based Features from Saccadic Eye-Movement as a Biometric in Radiology