A statistical sampling strategy for iris recognition
We present a new approach for iris recognition based on a random sampling strategy. Iris recognition is a method to identify individuals, based on the analysis of the eye iris. This technique has received a great deal of attention lately, mainly due to iris unique characterics: highly randomized ap...
Guardado en:
| Autores principales: | , , |
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| Formato: | Objeto de conferencia |
| Lenguaje: | Inglés |
| Publicado: |
2006
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| Materias: | |
| Acceso en línea: | http://sedici.unlp.edu.ar/handle/10915/24248 |
| Aporte de: |
| id |
I19-R120-10915-24248 |
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| record_format |
dspace |
| institution |
Universidad Nacional de La Plata |
| institution_str |
I-19 |
| repository_str |
R-120 |
| collection |
SEDICI (UNLP) |
| language |
Inglés |
| topic |
Ciencias Informáticas Identificación Biométrica iris recognition systems |
| spellingShingle |
Ciencias Informáticas Identificación Biométrica iris recognition systems Garza Castañon, Luis E. Morales Menéndez, Rubén Montes de Oca, Saúl A statistical sampling strategy for iris recognition |
| topic_facet |
Ciencias Informáticas Identificación Biométrica iris recognition systems |
| description |
We present a new approach for iris recognition based on a random sampling strategy. Iris recognition is a method to identify individuals, based on the analysis of the eye iris. This technique has received a great deal of attention lately, mainly due to iris unique characterics:
highly randomized appearance and impossibility to alter its features. A typical iris recognition system is composed of four phases: image acquisition and preprocessing, iris localization and extraction, iris features characterization, and comparison and matching. Our work uses standard integrodifferential operators to locate the iris. Then, we process iris image with histogram equalization to compensate for illumination variations.The characterization of iris features is performed by using accumulated histograms. These histograms are built from randomly selected subimages of iris. After that, a comparison is made between accumulated histograms of couples of iris samples, and a decision is taken based on their differences and on a threshold calculated experimentally. We ran experiments with a database of 210 iris, extracted from 70 individuals, and found a rate of succesful identifications in the order of 97 %. |
| format |
Objeto de conferencia Objeto de conferencia |
| author |
Garza Castañon, Luis E. Morales Menéndez, Rubén Montes de Oca, Saúl |
| author_facet |
Garza Castañon, Luis E. Morales Menéndez, Rubén Montes de Oca, Saúl |
| author_sort |
Garza Castañon, Luis E. |
| title |
A statistical sampling strategy for iris recognition |
| title_short |
A statistical sampling strategy for iris recognition |
| title_full |
A statistical sampling strategy for iris recognition |
| title_fullStr |
A statistical sampling strategy for iris recognition |
| title_full_unstemmed |
A statistical sampling strategy for iris recognition |
| title_sort |
statistical sampling strategy for iris recognition |
| publishDate |
2006 |
| url |
http://sedici.unlp.edu.ar/handle/10915/24248 |
| work_keys_str_mv |
AT garzacastanonluise astatisticalsamplingstrategyforirisrecognition AT moralesmenendezruben astatisticalsamplingstrategyforirisrecognition AT montesdeocasaul astatisticalsamplingstrategyforirisrecognition AT garzacastanonluise statisticalsamplingstrategyforirisrecognition AT moralesmenendezruben statisticalsamplingstrategyforirisrecognition AT montesdeocasaul statisticalsamplingstrategyforirisrecognition |
| bdutipo_str |
Repositorios |
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