Abstract
In a number of classification problems, the features are represented by histograms. Traditionally, histograms are compared by relatively simple distance measures such as the chi-square, the Kullback-Leibler, or the Euclidean distance. This paper proposes a likelihood ratio classifier for histogram features that is optimal in Neyman-Pearson sense. It is based on the assumptions that histograms can be modelled by a multinomial distribution and the bin probabilities of the histograms by a Dirichlet probability den- sity. A simple method to estimate the Dirichlet parameters is included. Feature selection prior to classification improves the classification performance. Classification results are presented on periocular and face data from various datasets. It is shown that the proposed classifier outperforms the chi-square distance measure.
| Original language | English |
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| Title of host publication | 2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS) |
| Place of Publication | Piscataway, NJ |
| Publisher | IEEE |
| Number of pages | 8 |
| ISBN (Electronic) | 978-1-5386-7180-1, 978-1-5386-7179-5 |
| ISBN (Print) | 978-1-5386-7181-8 |
| DOIs | |
| Publication status | Published - 21 Sept 2018 |
| Event | 2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems, BTAS 2018 - Torrance Marriott Redondo Beach, Los Angeles, United States Duration: 22 Oct 2018 → 25 Oct 2018 Conference number: 9 https://www.isi.edu/events/btas2018/home |
Publication series
| Name | IEEE International Conference on Biometrics Theory, Applications and Systems (BTAS) |
|---|---|
| Publisher | IEEE |
| Volume | 2018 |
| ISSN (Print) | 2474-9680 |
| ISSN (Electronic) | 2474-9699 |
Conference
| Conference | 2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems, BTAS 2018 |
|---|---|
| Abbreviated title | BTAS |
| Country/Territory | United States |
| City | Los Angeles |
| Period | 22/10/18 → 25/10/18 |
| Internet address |