A Criterion for Analysis of Different Sensor Combinations with an Application to Face Biometrics
| Autor: | Virginia Espinosa-Duro, Jiří Mekyska, Enric Monte-Moreno, Marcos Faundez-Zanuy |
|---|---|
| Rok vydání: | 2010 |
| Předmět: |
Biometrics
Computer science business.industry Generalization Cognitive Neuroscience Pattern recognition Mutual information Spectral bands Information theory Computer Science Applications Scoring algorithm Face (geometry) Pairwise comparison Computer Vision and Pattern Recognition Artificial intelligence business |
| Zdroj: | Cognitive Computation. 2:135-141 |
| ISSN: | 1866-9964 1866-9956 |
| DOI: | 10.1007/s12559-010-9060-5 |
| Popis: | In this paper, we propose a criterion for pairwise combination of information from different sensors in order to decide how a given pair of sensors is useful for different applications. This criterion is related to the principle of maximum information preservation. We present experimental results for the case of face images at different spectral bands, which allow for the in advance evaluation of the usefulness of different sensor combinations as well as the possibility for crossed-sensor recognition (matching of images acquired in different spectral bands). The criterion that we propose is a generalization of the Fisher score for the case of mutual information, which is measured as the ratio of the interclass information to the intraclass. The score we propose measures the behavior of a pair of sensors either when they are used in combination or when they are used to discriminate between classes. Based on Information Theory measurements, we conclude that the best spectral band combination always contains the thermal image, while the best combination for crossed-sensor recognition is VIS and NIR. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full text from SpringerLink